Giuseppe Di Lena
email: giuseppe.dilena@orange.com

Frédéric Giroire
email: frederic.giroire@cnrs.fr

Thierry Turletti
email: thierry.turletti@inria.fr

CloudTrace Demo: Tracing Cloud Network Delay

Keywords:

Many companies and organizations are moving their applications from on-premises data centers to the cloud. The cloud infrastructures can potentially provide an infinite amount of computation (e.g., Elastic Compute) and storage (e.g., Simple Service Storage). In addition, all cloud providers propose different offers: IaaS, PaaS, and SaaS. This demo focuses on the IaaS services, presenting a simple tool to measure the network delay in a virtual infrastructure built entirely in the cloud. These measurements are useful for organizations that are moving current applications to, or creating new applications in, the cloud, but have requirements on the maximum, or average, network delay that these applications can tolerate. We present CloudTrace, a simple CLI tool that creates regional and multiregional experiments to measure delay, using Amazon AWS.

I. INTRODUCTION

Cloud computing provides convenient on-demand access to a potentially unlimited pool of computing resources. In recent years, cloud computing resources have become cheaper and more powerful, thanks to the growing interest of companies around the world. Cloud computing brings several advantages: a company has little or no capital expenditure (CAPEX) when launching a new product, instead of having to invest in data centers and servers before knowing how much these resources are going to be used. It is flexible and on-demand. This means that the users can consume computing power and only pay for the amount of resources they actually consumed.

On a high level, cloud providers offer three service models, Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). We focus on IaaS, where users can manage the virtual instances and networks created in the cloud infrastructure, while the cloud provider is responsible for managing the physical infrastructure for providing a virtualization layer to isolate each user's environment.

We provide a brief overview of the main component of Amazon Web Services (AWS) that is responsible for this cloud model which is Amazon Elastic Cloud Computing (EC2). We describe all the virtual devices involved in the creation of a virtual infrastructure, and we apply the best practice strategies to deploy our experiments in a regional and multiregional environment. All major cloud providers offer resources for each instance flavor (e.g., t3.medium, m5.large, etc.), describing how many vCores and how much RAM are assigned. Regarding networking, AWS provides the maximum throughput that a flavor is able to generate (usually from 500 Mbps to 100 Gbps). AWS does not provide the maximum delay inside the infrastructure. It only mentions that all the regions and the availability zones are connected with a high performance optical fiber network.

The goal of this demo is to present a simple Command Line Interface (CLI) tool that can measure the network delay between two, or more, different networks in AWS using different deployment strategies.

II. RELATED WORK

Since AWS launches EC2 service [START_REF]EC2 instance types[END_REF], multiple approaches have been proposed to measure computing or network performances in virtualized environments and cloud infrastructures. Most of them focus on the impact of a virtualized environment with different hypervisors [START_REF] Pv | Evaluation of different hypervisors performance in the private cloud with SIGAR framework[END_REF], virtualization using Virtual Machines and containers [START_REF] Li | Performance overhead comparison between hypervisor and container based virtualization[END_REF], or the network performances variability in virtualized instances [START_REF] Gogunska | Tuning optimal traffic measurement parameters in virtual networks with machine learning[END_REF]. One of the most convincing measurement tools for the cloud environments is Cloudbench (CBTOOL) from IBM [7]. This tool automates experiments for a large range of workloads (e.g., Hadoop [START_REF]Hadoop docs[END_REF]) in cloud environments. In [START_REF] Jacquemart | Measuring the Amazon Web Services (AWS) WAN Infrastructure[END_REF], the authors performed largescale traceroute measurements over the global AWS network infrastructure. The traceroutes were performed between 15 regions, and the goal was to study the complexity of the AWS infrastructure. The work in [START_REF] Arnold | Cloud Provider Connectivity in the Flat Internet[END_REF] covers more generally the major cloud providers and the main Tier-1 ISPs. It shows how Internet traffic is more and more generated and shared with private interconnection between the cloud providers, thus bypassing the Tier-1 ISPs.

III. AWS BACKGROUND

The AWS global infrastructure is composed of 2 different entities: Region and Availability Zone (AZ).

The Availability Zone is a set of one or more data centers connected by redundant high-speed networks in order to guarantee high availability to the users. The region represents a set of multiple AZs clustered in a specific area and located around the world, within 100 km of each other. AZs in a same region are interconnected with high-bandwidth, lowlatency, high-throughput networks, over fully redundant and dedicated metro fiber. To guarantee privacy, all traffic between AZs is encrypted. If an application is partitioned across AZs, companies are better isolated and protected from issues such as power outages or natural disasters [START_REF]AWS Global Infrastructure documentation[END_REF]. AWS provides hundreds of different services. We describe briefly the main services and tools provided for IaaS. The first layer of an IaaS in AWS is the Virtual Private Cloud (VPC) that creates a 978-1-6654-0522-5/21/$31.00 ©2021 IEEE Fig. 1. Architecture in regional setup virtual network defined by the user. All virtual instances and virtual devices (i.e., virtual gateways or route tables) should be created within a VPC. The VPC is composed of one or more subnets. The user managing the subnets can create as many subnets as needed in a single VPC till the address space in the VPC becomes empty. To control network flows in the network subnet, the user can create route tables associated with them. If the instances are in the same VPC, it is possible to connect them directly using the private IP if they are in the same subnet. If they are in a different subnet, the route table should be configured before the connection (Fig. 1). By default, the VPC does not provide any way to connect the instances to the external network. The Virtual Internet Gateway is a highly scalable virtual device managed entirely by AWS that allows traffic to go from the VPC to the outside world and vice-versa. Once created, the route table should be updated to redirect traffic to the subnets. Two other main services are Network Access Control List (NACL) and Security Group (SG). NACL is a stateless firewall associated with an entire subnet, while SG is a stateful firewall associated with a virtual interface. There are three types of address in AWS: private, public, and Elastic IP. Elastic IP is a public address that can be conveniently attached to, and detached from, a virtual interface at any time. Once created and attached, the Elastic IP is not released even if the user deletes the instance or the interface. The address should be explicitly released by the user when it is not needed anymore. AWS proposes the VPC peering service [START_REF]Vpc peering aws documentation[END_REF] which makes it possible to connect two or multiple VPCs. Instances in the peering can connect between them via private IP, as if they are in the same network. Amazon ensures that the traffic going via the peering never leaves the AWS network, increasing security and performances. The last service is Elastic Compute (EC2), that provides secure and resizable compute capacity in the cloud, and proposes a large number of instance types [START_REF]EC2 instance types[END_REF]. For our experiments, we use different architectures (Figs. 1 and2). Fig. 1 describes a regional infrastructure composed of two subnets created in Fig. 2. Architecture in multiregional setup with VPC peering two different AZs within a single VPC, i.e., in a single region. As explained above, the Internet Gateway is responsible to allow connectivity with the external Internet. An instance is created on each subnet, and the flow table is stored in a single route table, serving the two subnets. The instances can connect to each other via the public IPs (an elastic IP is attached to each interface of the instance), or via the private IP since they are on the same VPC. In Fig. 2, the infrastructure is shared between two different regions. The architecture is similar, and the instances can still use the public IP to connect to each other like in the regional environment, but they cannot use by default the private IPs. In this case, a VPC peering connection between the two VPCs needs to be created to allow the instances to use the private IP to connect them, as if they were on the same network. Our tool automatically generates the infrastructure in both cases, with two or more AZs in case of regional and two or more regions in case of multiregional experiments.

IV. IMPLEMENTATION

CloudTrace is open-source, and it is compatible with Linux and MAC OSX, while it is possible to install it on Windows via Docker. The tool is built using LiteSQL, Ansible, Paris-Traceroute, and the Boto3 API for the automatic deployment in Amazon AWS. The basic idea is to have a CLI that takes as input the regions and the type of experiment that the user wants to run, and creates an environment performing multiple traceroutes between the virtual instances. First of all, CloudTrace creates a unique ID for the experiment (a string composed of 8 alphanumeric random characters).

Regional Deployment. The user has to specify only one region. The tool first checks if there is at least one VPC slot available in the region, then creates the VPC with subnet 10.0.0.0/16. Finally, it creates then a 10.0.X.0/24 subnet for each Availability Zone (AZ) available in the region. After setting up the subnets, it creates an Internet gateway and attaches it to the VPC. It then creates a route table and appends all the routes necessary to allow external connectivity to the future instances created within the subnet. To allow Internet connectivity, adding the route is not enough. The tool also adds the rules in the NACL. After configuring all the subnets and the NACL and SG rules, the tool creates an instance in each subnet.

Multiregional Deployment. The setup of the infrastructure is similar to the regional one. First of all, the tool connects with each region specified by the user and checks if there is at least one VPC slot for each region. If it is the case, it creates a VPC, a subnet and an Internet gateway in each region in the default AZ, and configures them like in the regional experiment. The VPC and the virtual subnet have the same subnet 10.0.X.0/24, because in case of VPC peering, the subnet cannot overlap.

If the user selects the option to use VPC peering, a peering connection between all the VPCs are created, and all the route tables are updated, in order to forward the traffic via the private IP between remote VPCs (Fig. 2). All the information needed is saved in the client database (i.e., public IPs, VPC ID, regions, instances ID, etc.). The tool parses all this information and creates for each experiment an Ansible inventory (a file that describes login info and addresses of the instances). For each experiment, Ansible runs a standard playbook, a YAML file that lists all the requirements the instances have to install and all the commands that each instance has to execute before running the experiment (e.g., update the kernel).

V. EXPERIMENTS

We present two experiments, tracing the network performances during one month in Amazon AWS, we will present in the demo how easily is to setup multiple experiments like these. The tests are made on two AWS regions: Frankfurt (eucentral-1) and London (eu-west-2), and ran from 04/11/2020 till 04/12/2020, where we collected in total more than one millions of traceroutes. Public vs. Private. AWS makes it possible to create a VPC peering between two or more different regions. This is done to connect two instances deployed in separated regions with the private IP assigned by AWS. As we can see, the delay using the private IP is higher, and also less stable, than for the public IP. This behavior is also repeated in all the regional experiments we performed. For example, Table I shows the delay with the confidence intervals in the Frankfurt region. We think that these differences are due to the additional layer added inside the AWS network and the control that each packet has to pass in order to circulate inside the AWS network.

Multiregional Experiments. We are performing the experiment using the public IP and AZ in different regions. Multiregional experiments mean long distances since the traffic has to go from Frankfurt to London, i.e., higher delays are expected.

VI. CONCLUSIONS

AWS provides hundreds of services to their customers. We considered the main EC2 components (e.g., VPCs, subnets, Virtual Internet Gateway) that companies and organizations use to move their infrastructures to the cloud. We mainly focused on the networking performances of the global infrastructure. Companies that are moving delay-sensitive applications on AWS are interested in the latency of the network in a high

Fig. 3 .

 3 Fig. 3. Eu-central-1a to eu-west-2a trace (public IP)

availability (multiregional) configuration or on a regional configuration with multiple Availability Zones. We implemented CloudTrace, a simple tool to measure the networking performances and automatically plot statistics, together with a map of the analyzed network. CloudTrace is publicly accessible at https://github.com/Giuseppe1992/CloudTrace.


Gwenaëlle André-Leroux

Samantha Samson

Delphine Cormontagne

Seav-Ly Tran

Solène Albert

Nalini Ramarao

Gwenaëlle André

The structure of bacterial Mfd dictates the pathogenicity in Bacillus cereus

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Mfd alignment in B. cereus separates pathogenic vs non pathogenic strains. Using an in vivo insect model of infection, we are currently testing if mfd gene of a pathogenic strain complements a non-pathogenic strain and viceversa, if Mfd gene of a non-pathogenic strain could complement the virulent phenotype of a pathogenic strain. Also, residues identified as substituted will be shortly site-directed mutated.

Method We did multiple sequence analysis (MSA) of a collection of B. cereus strains with conflicting pathogenicity signatures. The two most distant Mfd were homology modeled and analyzed with respect to solved structures of Mfd from E. coli 3-4 . To assess the relevance of the mutation ratio, we did MSA within the same collection for house-keeping genes with distinct expression level PykA and FtsZ.

Results

Based on Mfd sequences alignment, clinical and non pathogenic strains could be separated in two clusters.

In total, 28/1176 residues are distinct between the most distant strains S50 and S53. They located on UvrB homology module. To infer on putative coevolution, we performed a MSA for the protein partner UvrA. UvrA shows the same profile of pathogenic/non pathogenic as Mfd and a higher level of substitutions while PykA and FtsZ show hardly any mutations.

Interaction of UvrA/UvrB homology module has been modelled using solved complex from E. coli [3][4] . It requires the disruption of D2/D7 interaction but engages a conserved interface. Refinement through molecular dynamics with Gromacs is in progress.

MaiAGE 1 &

 1 Micalis 2 AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France. Samantha Samson 1 , Delphine Cormontagne 2 , Seav-Ly Tran 2 , Solène Albert 1,2 , Nalini Ramarao 2 and Gwenaëlle André 1 The structure of bacterial Mfd dictates pathogenicity in Bacillus cereus Mfd -Mutation frequency decline-protein is ubiquitous and involved in bacterial DNA repair. Mfd confers bacterial protection against the nitric oxid immune response mounted by the host during infection 1-2 . Mfd preserves DNA integrity, helps to repair DNA damage. Reversely, its neutralization empowers the host immune system. Issue: are there patterns in sequence, and 3D markers in structure of Mfd that could explain the clinic vs non-pathogenic phenotype of Bacillus cereus?

  excision repair complex gwenaelle.andre@inrae.fr

C. Darrigo, et al

2016, doi: 10.1371/journal.pone.0163321. 2 E. Guillemet, et al, "2016, doi: 10.1038/srep29349. 3 A. M. Deaconescu, et al, 2006, doi: 10.1016/j.cell.2005.11.045 4 A. M. Deaconescu, et al, 2012, doi:10.1073/pnas.1115105109


H E Belhouchet

M S Benzagouta

A Dobbi

E Mazouz

N Achi

Joelle Duplay

M Khodja

Reservoir Compartmentalization and Fluid Properties Determination via Modular Dynamic Tester (MDT): An application for a Case study from an Algerian Oil Field

Keywords: Porous medium, fluid types, Reservoir pressure, Mobility, Fluid contact

come

Reservoir compartmentalization and fluid property determination using a modular dynamic tester (MDT):

case study of an Algerian oil field

I. INTRODUCTION

The fluid flow, in a porous medium, is a very complex phenomenon. Identification and selection of the most appropriate solutions must be required to define the main parameters related to the porous medium [START_REF] Benzagouta | Reservoir Heterogeneities, in Fractured Fluvial Reservoirs of the Buchan Oilfield (Central North Sea)[END_REF]. Among these parameters are porosity and permeability. Based on literature reviews, several tools have been used to determine these parameters (O. [START_REF] Serra | Fundamentals of Well Log Interpretation[END_REF]. Concerning formation porosity, it can be determined using methods such as laboratory core analysis via gas or water injection. Logging tools can support the obtained laboratory results for porosity evaluation. Among these devices, apte to record porosity are the Computed Tomography (CT), Neutron Density Logging (C.N.L), sonic tools (∆t), Focused Density Log (FDC) and Nuclear Magnetic Resonance tools (NMR) [START_REF] Liu | Principles and Applications of Well Logging[END_REF].

Many reviewers were approaching the porous medium identification and their control on the reservoirs based on case studies (BENZAGOUTA, M . S. and LEE, M . R ., 1991), [START_REF] Benzagouta | Pressure and Temperature Effect on Petrophysical Characteristics: Carbonate Reservoir Case[END_REF], [START_REF] Kharraa | Assessment of Uncertainty in Porosity Measurements Using NMR and Conventional Logging Tools in Carbonate Reservoir[END_REF], [START_REF] Attia | Petrophysical analysis and hydrocarbon potentialities of the untested Middle Miocene Sidri and Baba sandstone of Belayim Formation, Badri Field, Gulf of Suez, Egypt[END_REF]. However, permeability can also be determined from core data, as it can be estimated from N.M.R. and well test interpretation. Similar operating processes are mainly used during the exploration and evaluation activities. Drill Stem Test (DST) and Wire Line Formation Tester (RFT/MDT/XPT/RCI) constitute the principal devices for outcomes. Development activities such as pressure transient testing can also be a close part of the investigation [START_REF] Belhouchet | Rock Typing: Reservoir PermeabilityCalculation Using Discrete RockTyping Methods (DRT): Case Studyfrom the Algerian B-H Oil Field Reservoir[END_REF].

Within the porous medium, identification of the stored fluid type is also required, to evaluate fluid volume. In oil and gas industry exploration and production, several techniques were worked out to define fluid type, its location and level of contact. Data coming out from well logs such as NMR, resistivity, density and neutron logs, combined with MDT pressure measurements and fluid samples, yield positive identification of formation fluids type. Additionally the outcomes are allowing determining the oilwater contact (O.W.C.) as well oilgas contact (O. G.C.), and gaswater contact (G.W.C.) [START_REF] Abudeif | Hydrocarbon type detection using the synthetic logs: A case study, Baba member, Gulf of Suez, Egypt[END_REF]. It is important to mention the distinction between the FWL (free water level) is generally not matching the OWC (oil water contact): they differs by an amount related to the displacement pressure [START_REF] Elshahawi | Capillary Pressure and Rock Wettability Effects on Wireline Formation Tester Measurements[END_REF].

The investigation applied in our case study was through Wireline formation tester (WFT) which is introduced to the oil industry in 1955 [START_REF] Conrad | APPLICATION OF THE WIRELINE FORMATION TESTER[END_REF]. It was conducted to estimate key parameters such as initial reservoir pressure, local permeability and reservoir fluid type [START_REF] Abudeif | Hydrocarbon type detection using the synthetic logs: A case study, Baba member, Gulf of Suez, Egypt[END_REF]. Support in that circumstances is from [START_REF] Dubost | Integration of In-Situ Fluid Measurements for Pressure Gradients Calculations[END_REF] and [START_REF] Elshahawi | Capillary Pressure and Rock Wettability Effects on Wireline Formation Tester Measurements[END_REF]. The use of these parameters can be essential resources for conducting research on the preferential fluid flow stored within the porous medium and furthermore exploring the possibility of perceiving permeability barriers.

Different tools can be used for the purpose of fluid behavior related to the porous medium. Repeat Formation Test (RFT), Reservoir Characterization Instrument (RCI), Modular Formation Dynamics Tester (MDT) and Formation Pressure test log (XPT) are formation tester and sampling tools. They record the formation pressure versus depth at various interval points in the open hole using a wire-line. These tools are also aimed to collect fluid samples [START_REF] Dybdahl | A systematic Approach to Sampling during Well Testing[END_REF], [START_REF] Radwan | Geopressure Evaluation Using Integrated Basin Modelling, Well-Logging and Reservoir Data Analysis in the northern part of the Badri oil field, Gulf of Suez, Egypt[END_REF].

In our case investigation and among the previously identified devices, it is the modular dynamic tester (MDT) which has been in use. The concerned tool was conducted for formation evaluation of pressure, temperature, and reservoir quality. One of its aims is the full understanding of the static and dynamic reservoir behavior [START_REF] Worthington | Optimizing the Value of Reservoir Simulation through Quality Assured Initialization[END_REF]. Formation pressure analysis is very interesting to approach. It is useful in providing information on reservoir quality and compartmentalization (James [START_REF] Howes | WFT Data and Pressure Profile Analysis: Applications, Complexities, and Challenges[END_REF]. The MDT is correspondingly an accurate tool for delivering statements on reservoir fluid type and free water level (FWL) [START_REF] Belabed | Gas-Water Contacts, Free Water Levels analysis in support of petroleum exploration in offshore Netherlands[END_REF].

At the present time, the use of the MDT tool has become indispensable for the development of the oil and gas fields because it provides basic information on the type and number of reservoir fluids and their contacts. The vertical recording made by the MDT provides pressure and mobility measurements at specific points, chosen according to the log interpretation [START_REF] Burnie | MDT/RFT Pressure Testing: Reservoir Interpretation, Supercharging Evaluation, and Data Processing[END_REF]. For accurate readings, points with low mobility and huge values are automatically eliminated. Their removal is own to problems related to the supercharged phenomenon, coupled to severe petrophysical characteristics, degradation, tightness, with sensor blockage [START_REF] Proett | [END_REF] and [START_REF] Basioni | A Case Study on Compositional Reservoir Simulation for a large Tight Reservoir: A Detailed Road Map from Initialization and History Match to Prediction Simulation[END_REF]. Own to accurate representative, and indicative pressure gradients, the elimination of non-significant points, along the concerned reservoir section, must be necessary to state.

In this investigation, the use of pressure gradients is aimed to investigate on the communication between boreholes. Similar task will provide information on the reservoir depletion when drilling a well subsequent to certain production time [START_REF] Cribbs | Practical Wellbore Formation Test Interpretation[END_REF]. Moreover, pressure gradient change analyses constitute an enhancing support for estimating the original hydrocarbon in place as well as the aquifer activity and assessment. Obtained gradients number will be characterizing fluids types based on their respective setting density values. Thus, a change in pressure gradient will be related to the change in the reservoir fluid type. Hence, the intersection between the two fluid gradients can be interpreted as a credible contact, separating two immiscible fluids in which capillary pressure should be zero. Similar statement is well supported by [START_REF] Fawakhir | Procedures for Initializing Reservoir Simulation Models[END_REF] and [START_REF] Elshahawi | Capillary Pressure and Rock Wettability Effects on Wireline Formation Tester Measurements[END_REF].

Application of the MDT, in the purpose of pressure determination and setting aimed to various gradients evolution, has become necessary for the case study. The pressure distribution is brought up to the well communication probe. Thus, lateral and vertical reservoir continuity and connectivity, with potential fluid contact determination constitute a key factor to be set in the reservoir. The use of data generated from the MDT can also be aimed to permit setting and extending limits for the free water level (F.W.L.) respective for each borehole composed zones. Related tasks will allow finding out the free water level outspread with respect to the surrounding boreholes. Similar outcomes will conduct reaching an overview concerning reservoir heterogeneity. Therefore, the basis of this investigation is stressing on a framework for reservoir compartmentalization in multilayer reservoirs. Therefore, the main objectives of this case study can be set according to available data generated from the MDT as:

-Determination of unconnected reservoir layers.

-Identification of reservoir fluid types and fluid gradient contact setting.

-Determination of fluid type regions.

-Determination of the free water level (F.W.L.) and the identification of the referred pressure.

Application of this investigation is conducted towards some of the Hassi Berkine oil field reservoirs (Algeria). As the concerned reservoirs are involved with similar ambiguous states.

II. GEOLOGICAL SETTING

The Hassi Berkine field was discovered in January 1995 by the SONATRACH/ANADARKO association [START_REF] Lo | Application of the Novel Miscible Interpretation of RST Data and the WAG Pilot Results in Reservoir Simulation for Hassi Berkine South Field[END_REF]. the Berkine Basin is located in south -eastern Algeria, between latitude 29 degrees and 33 degrees north and longitude 5 degrees and 9 degrees East [START_REF] Belhouchet | A new empirical model for enhancing well log permeability prediction, using nonlinear regression method: Case study from Hassi-Berkine oil field reservoir -Algeria[END_REF]. It is limited to the north by the southern border of the Dahar Mole, to the south by the Mole D'Ahara which separates it from the Illizi basin. The concerned basin is bounded to the East by the Tunisian -Libyan borders and finally to the west by the structural extension north of the Amguide -El Biode -Hassi Messaoud Mole. The Berkine Basin is a part of the northern margin of the Saharan platform. It is an intra -or pericratonic basin which has been developed during Middle-Late Triassic times [START_REF] Turner | Sequence stratigraphy and sedimentology of the late Triassic TAG-I (Blocks 401/402, Berkine Basin, Algeria)[END_REF] .

The main reservoir in this field is the Lower Triassic Clayey Sandstone (TAGI, French abbreviation for Trias Argilo-Gréseux Inferieur). The thickness of the TAGI varies from 65 meters to 130 meters. TAGI is of a transgressive nature and lies unconformably on the Paleozoic formations. It is mainly represented by fluvial and lacustrine deposits. Sometimes these deposits are interbedded by aeolian and deltaic layers. Based on the extensive core data and regional interpretation of wireline logs (Benamrane [START_REF] Oussalem | 'EXPLORATION DES PIEGES STRATIGRAPHIQUES D'HYDROCARBURES : APPLICATION AU TRIAS DU BASSIN DE OUED MYA -ALGERIE[END_REF], [START_REF] Boudjema | Évolution structurale du bassin pétrolier "triasique" du Sahara Nord oriental (Algérie)[END_REF] the TAGI reservoir could be subdivided into three main levels; upper, middle and lower. These levels are subdivided into a total of nine layers (figure 1).

Figure 1: Reservoir subunits in TAGI [START_REF] Sonatrach | Well Evaluation Conference (WEC) Algeria[END_REF] Detailed sedimentological and stratigraphic subdivision of the TAGI is as follow is according to [START_REF] Boudjema | Évolution structurale du bassin pétrolier "triasique" du Sahara Nord oriental (Algérie)[END_REF], [START_REF] Aït-Salem | Le Trias détritique de l'Oued Mya (Sahara algérien) : sédimentation estuarienne, diagenèse et porogenèse, potentialités pétrolières[END_REF], [START_REF] Sonatrach | Well Evaluation Conference (WEC) Algeria[END_REF] and [START_REF] Sayed | Caractérisation lithologique et pétrophysique (méthode analogique et IP) D'un niveau réservoir Trias Argileux Gréseux Supérieur¨[END_REF].

The upper TAGI consisting of:

-U1a a sandstone level characterized by fluviatile deposits traversed by eolian deposits. This has a good lateral extension. -U1b a sandstone level whose reservoir qualities deteriorate toward the southeast. It also includes some interbedded deltaic deposits. -U2 is the clay which is assumed to be regionally continuous and characteristically has small thickness. It is also marked by lacustrine deposits. -U3 of variable thickness, in which the first sandstone beds interbedded with deltaic deposits are encountered.

The middle TAGI is subdivided into four levels:

-M2 clayish unit, which is assumed to be a lacustrine deposit and of regional extent. It is of very variable thickness. -M1c, M1b, and M1a, which are sandstone having generally good reservoir characteristics. They are marked by variable thicknesses according to the local distribution of channels.

Level M1 shows the greatest variation in thickness.

The lower TAGI, which lies directly on the hercynian unconformity, is subdivided into two levels -L5 a sandstone characterized by fluviatile deposits.

-L6 representing the clay.

In the conducted case, an investigation is concerned with TAGI formation assembling three wells: well-1, well-2 and well-3 belonging to the Hassi Berkine basin (figure 2). The reservoir characteristics analysis has been found of relatively up to good quality porosity.

III. MATERIAL AND METHODOLOGY OF INVESTIGATION

Interest in the case study area were wells 1, 2 and 3 drilled in the area of Hassi Berkine oilfield basin (figure 2). Available data includes pressure data recorded using Modular Formation Dynamics Tester (MDT) or Formation Pressure Tester Tools (F.P.T.T.). These tools are aimed to provide along the concerned reservoir section, pressure and mobility measurements. The recorded data is established at specific selected points, according to the well log interpretation.

The number of selected points could be depending on the thickness of the reservoir layer. According to the tool efficiency (MDT) in the case study, four to six points can be selected for each reservoir layer, depending on the tasks. Restriction in this methodology procedure is to automatically eliminate data with low or huge mobility values. This task is intended to avoid supercharged phenomenon such as: severe petrophysical characteristics degradation, tightness and the sensor blockage. The elimination of non-significant points must be necessary to have a representative and indicative pressure measurements and gradients. Table (see #Appendix) is indicator of the considered data in that purpose.

Conducted investigation can be referred to reservoir pressure parameter. This considered factor can be a useful device in indicating a static linear-gradient contingent on the fluid's density (water, brines, oil, and gas) in the pore spaces [START_REF] Selim | Reservoir vs. Seal Pressure Gradients: Perception and Pitfalls -Based on Case Histories from the Gulf of Mexico[END_REF]. Support in that purpose can be through the pore pressure of the reservoirs [START_REF] Radwan | Pore and fracture pressure modeling using direct and indirect methods inBadri Field, Gulf of Suez, Egypt[END_REF]. Stated equations are as follow:

𝑃 𝑧 = 0.433 * 𝐹𝑙𝑢𝑖𝑑 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 * 𝑍 (1)
Thus, the gradient can be calculated by Fooshee, Jeffrey Steven, 2009:

𝐹𝑙𝑢𝑖𝑑 𝐺𝑟𝑎𝑑𝑖𝑒𝑛𝑡 (𝑝𝑠𝑖 𝑓𝑡 ⁄ ) = 𝑃 𝑧 𝑍 = 0.433 * 𝐹𝑙𝑢𝑖𝑑 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 (2)
𝑃 𝑧 ; Pressure at depth Z; Depth Using the equation ( 2), the fluid density can be calculated based on gradient observed graphically according to the following formula:

𝐹𝑙𝑢𝑖𝑑 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 = 𝐹𝑙𝑢𝑖𝑑 𝐺𝑟𝑎𝑑𝑖𝑒𝑛𝑡 0.433 ⁄ (3)
Oil-field liquids and gases occur in a wide range of compositions. The table below shows typical density ranges and gradients for gas, oil, and water. However, own to exceptions occurrences, some expected fluid types might present their own appropriate values [START_REF] Fooshee | The development of a pore pressure and fracture gradient prediction model for the Ewing Banks 910 area in the Gulf of Mexico[END_REF]. Thus, fluid density outcomes in our case study can be derived from (Table 1).

IV. RESULTS AND INTERPRETATION

In order to calculate the pressure gradients at the wells drilled from the MDT data, quality control of the recorded points must be carried out. The led process for the case study was based on the pressure quantity recorded in relation to the actual pressure. The oversized pressures have been eliminated automatically. This subtraction depends on the measured mobility: Specifically points of low mobility, their occurrence can be own to the degradation of petrophysical characteristics. The dimensionless mobility points were related to the presence of fractures or sealing problems. These two cases of mobility; minimal and dimensionless were directly eliminated. The remaining points have constituted the most representative measurements. They afford a better illustration of the pressure gradients giving a possibility to identify the static and dynamic reservoir behaviors. According to the MDT obtained results for the case study, three main interpretation steps were set.

A. Determination of unconnected reservoir layers and reservoir fluid type

The first step consists of locally processing the MDT records. The obtained data from exploration wells are intended to determine the number of unconnected reservoir layers and identify the type of reservoir fluid. Different boreholes were considered in the purpose of obtaining maximum information on fluid distribution and contact in addition to reservoir heterogeneities [START_REF] Belhouchet | A new empirical model for enhancing well log permeability prediction, using nonlinear regression method: Case study from Hassi-Berkine oil field reservoir -Algeria[END_REF].

1) WELL-1

Well-1 is the first exploration well drilled in the north west of the field. The well location and pressure reordered from formation pressure tester (MDT) are illustrated in figure 3. Results and interpretation, based on formation pressure tester, for the case study, have been found very significant towards reservoir type. Different compartments were defined on the basis of pressure versus depth (figure 3). Using this tool, in the case study, is own to its performance in that considered topic [START_REF] Noah | Use Repeat Formation Tester for Determination of Some Reservoir Characteristics for Kareem Formation in Some Wells at Amal Field, Gulf of Suez Area, Egypt[END_REF].

The acquired compartments revealed more useful information on the possible heterogeneities of the reservoir, which can be closely related to the presence of a number of permeability barriers (figure 4).

According to the graphical analysis, with pressure versus depth, several pressure gradients with statistically significant trends have been found, for the same considered well (figure 3). The range of pressure gradients occurring in the first exploration well might be related to the presence of two distinct reservoir layers being separated by shale barriers (figure 4). Based on graphical interpretation, with pressure versus depth, three pressure gradients were defined: 0.34 psi/feet in the upper TAGI, 0.47 psi/feet in the middle TAGI and 0.3 psi/feet in the lower TAGI. In order to estimate the equivalent density, equation 3 was applied. Depending on the results obtained, the reservoir layers were characterized by three different reservoir fluids based on their specific density: the first fluid with a density of 0.78 kg/l in TAGI-U, in TAGI-M, the fluid characterized by a density of 1.08 kg/l, and the third fluid in TAGI-L with a density of 0.69 kg/l (Table 2). Table 2: Pressure gradients and fluid densities interpreted for the Well-1

2) WELL-2

This well was drilled in the central part of the field with the aim of crossing all the reservoir sections for collecting useful information, and to acquire pressure gradients in the desired area. The location of the concerned wells with the MDT records is shown in figure 5. Graphical analyses, using the formation pressure tester records, for well-2, revealed the same results as in well-1. Two separately gradients have been selected: first in the upper and middle reservoir part and the second gradient was pointing up along the lower considered reservoir section (figure 5). The pressure gradients, presented in the upper and middle reservoir sections, show that the fluid reservoir could be the oil according to the defined gradient 0.3048 Psi/feet. This gradient is similar to that gradient observed in the TAGI-L (well-1), and therefore, the same fluid has been proven in the two reservoir levels TAGI U+M for the well-2 (table 3) and TAGI-L for the well-1 (table 2). Although the presence of a single point in the lower part of the reservoir gives information on the pressure dimension, but it remains insufficient for determining the pressure gradient for this reservoir level.

An identical gradient has been identified in the upper and middle reservoir sections (figure 5). It means that these upper and middle reservoirs can be treated as in dynamic communication. Assuming that, the same type of reservoir fluid was presented in the TAGI-L. Additionally and according to the gradient profiles passing through the points recorded in the TAGI U + M and the TAGI-L at depth -9200 feet, a difference of 58 Psi was observed between the Upper-Middle and Lower reservoir sections (figure 4). This distinction can be obviously related to the presence of a likely lithologic barrier between the two zones (figure 6). Table 3: Pressure gradients and fluid densities interpreted for the well-2

3) WELL-3

After confirmation of the presence of the two reservoir fluids, a third well has been proposed to be drilled in the aquifer zone. Among objective in that purpose was the topping of the free water level (F.W.L.) in both reservoirs; upper, middle and lower part. The location of the well and pressure reordered from formation pressure tester (MDT) is illustrated in figure 7. Regarding the figure 7, the formation pressure tester analysis (F.P.T.A.) shows the same pressure gradient profile for the upper, middle and lower reservoir sections. The implication of the gamma ray log is indicator of a stratigraphic barrier between these reservoir levels (figure 8). This statement can be, in terms of lithology, a stratigraphic barriers being revealed using well logs, Implication of pressure gradients which is the same, essentially in the TAGI-M and TAGI-L, is revealing that the three reservoir levels have been saturated by the same fluid type, a communication path is probable. Thus, the stratigraphic barrier is discontinuous or disjointed. The recorded pressure gradient in this well is about 0.51 psi/ft. Similar value is corresponding to water fluid in table 4. Thus, this is revealing that the drilled borehole reservoir is a water fluid type. Depending on the location of the aquifer zone in this considered borehole, recommended is to use its own specific data information for establishing the free water level (F.W.L.) and its extend.

B. Fluid Type (PVT) Regions Identification

Based on the pressure gradients defined in the previous section, the density of the reservoir fluid was calculated for each reservoir level. According to the obtained results, several reservoir fluid types have been expected. Similar results constitute the main support for going through reservoir compartmentalization. Thus, the need for the determination of the fluid type regions should be necessary.

An analytical examination of MDT pressure recorded is showing the presence of two oil gradients which are: 0.34 psi/feet and 0.3 psi/feet. Based on these gradients, the densities of reservoir fluids were calculated by applying the equation 3. The first estimated density is 0.78 kg/l in the upper TAGI located in the western zone of the field (well-1 zone). The second extracted density is 0.69 kg/l corresponding to the lower TAGI, situated in the same zone (well-1 zone). The same density of 0.69 kg/l has been obtained for TAGI U+M in the eastern zone (well-2 zone). In view of that results, two PVT regions were defined; PVT region 1 in the upper TAGI of the western area of the field (TAGI-U), and PVT region 2 in the lower TAGI of the western area, and TAGI U+M for the eastern area of the field. For accurate support and statement deduction, summarized pressure gradients and estimated fluid density outcomes are sited in table 5.

C. Free Water Level and Reference Pressure Determination

The third phase is effectively based on the general view of the field. It focuses mainly on exploiting the gradients measured at all exploration boreholes to determine the free water level (F.W.L.) and the referred pressure. The free water level identification (F.W.L.) is one of the main variables used to define the reservoir compartments. To determine the F.W.L., the use of M.D.T. data, prior to production starting-up, must be required essentially. The first step in this action is to interpret the formation pressure tester data to define reservoir compartments at each well. The graphical analysis of pressure gradients, measured and recorded from the two drilled wells (well-1 and well-2), has shown that TAGI reservoir could be subdivided, according to pressure profile, into two compartments: the first compartment corresponds to the upper -middle reservoir section, whereas the second compartment can be allocated to the lower part (figures 3 and 5).

The combination of the different pressure gradients versus depth, derived from the various neighboring boreholes, permit the set of the free water level (F.W.L.) at each considered reservoir. The graphical analysis of the formation pressure tester data shows the presence of two possible free water levels, the first one is located in the uppermiddle TAGI and has been set at -9147.5 feet, and the second in the lower TAGI is expected at -9360.6 feet (figure 9). For precision, pressure equation parameters, used for F.W.L. determination for TAGI reservoir levels, are illustrated in tables 6 and 7. Regarding the well gradients observed in the two wells (1 and 2), the pressure gradients characteristics are superimposed with a slight deviation. This change in gradient can be explained by the possible petrophysical characteristics degradation affecting differentially those two wells. It can also be related to the change in reservoir fluid type, especially in the TAGI-U. In terms of pressure similarities, these wells may suggest a scenario of being in a dynamic communication (table 8).

  It is varying from 7 to 20 % whereas the permeability is found fluctuating from 1 millidarcies to 850 millidarcies (mD)[START_REF] Belhouchet | A new empirical model for enhancing well log permeability prediction, using nonlinear regression method: Case study from Hassi-Berkine oil field reservoir -Algeria[END_REF]. The initial development plan called a first stage of production during the first 5 years was at the rate of 5000 Stb/day. This production is supported by water injection at the reservoir peripheral. Initial well tests carried out on the well-2 were indicator of an excellent sandstone reservoir (TAGI) with a flow of 4500 Stb/day. It is important to mention that no water cut (WCT) has been observed.

Figure 2 :

 2 Figure 2: Wells position map

Figure 3 :Figure 4 :

 34 Figure 3: Well location and pressure reordered from formation pressure tester (MDT) for Well-1

Figure 5 :Figure 6 :

 56 Figure 5: Well location and recorded pressure from formation pressure tester (MDT) for well-2

Figure 7 :

 7 Figure 7: Well location and pressure reordered from formation pressure tester (MDT) for well-3 with focus on water setting level

Figure 9 :

 9 Figure 9: Formation pressure tester results recorded in the upper -middle and lower TAGI reservoirs of the exploration wellsTAGI U+M Oil equation Water equation A 3.5448 A' 1.9556 B -7399.5 B' 18.826

Table 1 :

 1 Table showing the fluid density and Gradient ranges for different fluids (AAPG WIKI, 2015)

	Fluid	Normal Density range (g/cm3)	Gradient (psi/feet)
	Gaz (Gazeux)	0.007 -0.3	0.003 -1.13
	Gaz (liquide)	0.2 -0.4	0.09 -0.174
	Oil	0.4 -1.2	0.174 -0.486
	Water or Brine	1 -1.15	0.433 -0.50

Table 4 :

 4 Pressure gradients and fluid densities interpretation for the well-3

	Wells	Level	Gradient (psi/feet)	d(lb/gal)	d(kg/m3)	d(kg/l)	Fluid
	WELL-3	TAGI-U	0.51	9.80769	1175.22073	1.18	Water
	WELL-3	TAGI-L	0.51	9.80769	1175.22073	1.18	Water

Table 5 :

 5 Pressure gradients and deduced fluid densities for the considered case study boreholes

	Wells	Level	Gradient (psi/feet)	Fluid Density (lb/gal)	Fluid Density (kg/m3)	Fluid Density (kg/l)	Fluid Type
	Well-1	TAGI-U	0.34	6.53846	783.48048	0.78	Oil
	Well-1 TAGI-M	0.47	9.03846	1083.04655	1.08	Water
	Well-1	TAGI-L	0.3	5.76923	691.30631	0.69	Oil
	Well-2	TAGI-U	0.3	5.76923	691.30631	0.69	Oil
	Well-2 TAGI-M	0.3	5.76923	691.30631	0.69	OIl
	Well-3 TAGI-M	0.51	9.80769	1175.22073	1.18	Water
	Well-3	TAGI-L	0.51	9.80769	1175.22073	1.18	Water

Table 6 :

 6 Pressure equation parameters used for F.W.L. determination for TAGI U+M

			TAGI-L
		Oil equation		Water equation
	A	3.2808	A'	2.0395
	B	-6304.1	B'	-377.3

Table 7 :

 7 Pressure equation parameters used for F.W.L. determination for TAGI-L

Similar graphical results have allowed the estimation of the initial pressure for each reservoir layer respectively: 4668 Psi in upper and middle sections and 4775 Psi in the lower section (table 8).

Regions

V. CONCLUSION

Modular dynamic tester (MDT), in relation to pressure evolution versus depth, constitutes a useful tool to be used efficiently in order to split up the limit between the different fluids, stored in the porous medium. Variation in pressure gradient is an interesting parameter being collected and used for information regarding fluid characteristics type and possibly lithology nature. These pressure gradients were also an adequate device to reveal the possible dynamic communication between the boreholes. In addition, modular dynamic tester was a successful device to set gradients change aimed to especially establish not only the different fluid contacts including the free water level (F.W.L.) but also the vertical and lateral ambiguities regarding fluid dynamism and heterogeneities.

The prediction of the type, number and physical characteristics of the reservoir fluid, and initial reservoir pressure by MDT tools constitute the main factors for the surface facilities aspects.

Reservoir compartments, reference pressure, free water level, PVT regions determination are the main inputs for reservoir model initialization. The overall has got a significant impact on the quality of simulation results.

The determination of the free water level (F.W.L.) reflects the initial conditions of the field that have a very significant impact on the estimation of the original oil in place (OOIP). Accordingly, this F.W.L. can be considered as one of the main factors involved for the field development plan preparation.

VI. FUNDING SOURCES

The authors engaged themselves to declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

VII. CONFLICT OF INTEREST STATEMENT

The authors have no conflict of interest to report.

IX. APPENDIX


Delphine Cormontagne

Seav-Ly Tran

Samantha Samson

Solène Albert

Gwenaëlle André-Leroux

Nalini Ramarao

The sequence of the universal bacterial DNA repair protein Mfd dictates the pathogenicity of Bacillus cereus strains

The bacterial protein Mfd (mutation frequency decline) is highly conserved among bacteria and has been shown to be involved in bacterial DNA repair. We have recently shown that this protein confers bacterial resistance to the host nitrogen response produced during an infection by the immune system. Indeed, Mfd helps preserving DNA integrity and is involved in the repair following NO-induced DNA damage. Using a large collection of Bacillus cereus strains with various pathogenicity, we could further establish a link between Mfd sequences, protein 3D structures and virulence. Protein sequence comparison and 3D modelling were used to determine specific domains involved in the virulence of B. cereus. Using an in vivo insect model of infection, we show that the mfd gene of a pathogenic strain could complement the avirulent phenotype of a non-pathogenic strain. These data strongly suggest that the 3D structure of Mfd plays an essential role in its function and might be a new and interesting way to discriminate pathogenic from harmless B. cereus strains.

As Mfd is widely conserved within bacteria, those findings could improve our understanding on the pathogenicity of potentially a large spectrum of bacteria.


Joelle Alquraishi

Duplay

A New Empirical Model for Enhancing Well Log Permeability Prediction, Using Nonlinear regression method: Case study from Hassi-Berkine Oil Field Reservoir -Algeria

Keywords: Permeability, Reservoir characterization, Rock typing, Hydraulic unite, Flow zone Indicator

A new empirical model for enhancing well log permeability prediction, using nonlinear regression method: Case study from Hassi-Berkine oil field reservoir -Algeria

Introduction

The development of an oil field is an entire range of research activities starting from exploration up to field development [START_REF] Galard | A case study on Redevelopment of a Giant highly fractured Carbonate Reservoir in Iran based on integrated reservoir characterization and 3D modeling studies[END_REF]. It consists of characterizing a sedimentary basin in detail. The exploration steps may involve seismic data, logging, and analysis up to geobodies identification. In order to get a stable, consistent and coherent model, specific studies have been developed [START_REF] Al-Hajeri | Basin and Petroleum System Modeling[END_REF]. Dynamic modeling with reservoir fluid characterization, SCAL studies, and identification of initial conditions are also part of these orientations [START_REF] Holtz | Residual Gas Saturation to Aquifer Influx: A Calculation Method for 3-D ComputerReservoir Model Construction[END_REF]. The reservoir characterization parameters in that purpose can be figured out by factors such as seismic attributes, rock typing and geostatic. These parameters concern can be through probabilistic function responsible for uncertainties. Different researches on that purpose have been led. However, investigations can be exposed to some risks. Doubts can be issued from a reduction of exploration uncertainties and inefficiency related to the predicted potential and real reserves presence of hydrocarbons (Richard C. [START_REF] Selley | Elements of petroleum geology[END_REF]. In addition, any exploration achievement can be related to significant geologic elements processes and rock typing [START_REF] Al-Hajeri | Basin and Petroleum System Modeling[END_REF]. Modeling system and fluid dynamics behavior can be key factors suitable for the reservoir potential evaluation and recovery challenge [START_REF] Galard | A case study on Redevelopment of a Giant highly fractured Carbonate Reservoir in Iran based on integrated reservoir characterization and 3D modeling studies[END_REF].

The Rock typing is a process of rock classification based on mineralogical composition, grain size, shape and pore size distribution (PSD). Rock typingfluid interaction, with fluid dynamic behavior, and capillary effect are of importance in this organization. In such circumstances, the envisaged process can involve: integrating, analyzing and synthesizing the real data supplied from different borehole records and cores analysis.

Other detailed parameters used, to define rock typing as part of the reservoir comprehension, characterization for fluid circulation aptitude is the hydraulic flow units (HFU). Rock typing classification can be allocated to the same dynamic properties, the same porosity, and permeability correlations results, identical capillary pressure profile and the same relative permeability curves. Similar characteristics are well supported by [START_REF] Shenawi | Development of Generalized Porosity-Permeability Transforms by Hydraulic Units for Carbonate Oil Reservoirs in Saudi Arabia[END_REF]. The cited authors found that Hydraulic Unit (HU) determination and use is providing a useful and interesting advice and device for rock typing definition in relation to the porous media assessment.

Once the rock typing identification procedure is done, reliable estimated permeability can be extended to the non-cored wells. This assumption allows the generation of water saturation models and minimizing convergence problems. Therefore, the reservoir performance is then related to generating simulation sensitivities, involving possible rock classes endorsed by considered scaled field. Accordingly, figuring out reservoir properties, it can be carried out along the whole reservoir sections.

Therefore, the requirement of conceptual studies in that regard is in need of sensitive steps materializing the stability and convergence of the model as a digital one. For the case study, the reservoir characterization, based on permeability (K) determination will lead to developing an operational modeling device. This application is capable of calculating the permeability as a function of porosity and lithology [START_REF] Enaworu | PERMEABILITY PREDICTION IN WELLS USING FLOW ZONE INDICATOR (FZI)[END_REF]. The impact of the rock properties on the static and dynamic behavior of the fluid circulation, related to rock typing, is another essential controlling factor to consider [START_REF] Attia | Identification of Barriers and Productive Zones Using Reservoir Characterization[END_REF].

Control on permeability prediction for rock typing determination is also related to the flow zone indicator (FZI). It is known that the FZI parameter is directly associated to the hydraulic unit for considered rock type as well as its porous media [START_REF] Enaworu | PERMEABILITY PREDICTION IN WELLS USING FLOW ZONE INDICATOR (FZI)[END_REF]. The method was proposed to decrease the absolute error between defined permeability from cores and the calculated one. In addition, applied method can ensure better development of the considered oilfield by optimizing the perforation intervals. With regards to the overall, an enhancement boreholes recovery can be set. Meanwhile, the method will guide avoiding the interval located in the aquifer section: Since underestimation of the permeability in the section near the oil-water contact (OWC) can be considered as the main issue for water breakthrough occurrence.

The preferred zone for perforation is generally carried out in interval above the oil-water contact. This scenario is intended to delay the water breakthrough arrival. On the other hand, owing to a similar scenario, the probability of having a good permeability in the reservoir section above the water-oil contact can be probably underestimated. Calculated permeability is affected by its estimation accuracy [START_REF] Wu | A New Method to Predict Water Breakthrough Time in an Edge Water Condensate Gas Reservoir Considering Retrograde Condensation[END_REF]. The calculated permeability care can be subdued to some uncertainties regarding its estimation exactitude. This kind of statement might be proofed by water-wet rocks and high salinity formations [START_REF] Elraies | INVESTIGATION OF WATER BREAKTHROUGH TIME IN NON-COMMUNICATING LAYERED RESERVOIR[END_REF].

The general complexity for the reservoir characterization related to permeability prediction especially in the non-cored borehole section, is conducted towards a variety of methods, which can be executed. Investigating on permeability and porosity characteristics, with regards to geological properties of the rocks, might carry considerable reserves. Texture and structure factors with mineral composition and their distribution (arrangement, packing, sorting distribution), remain an essential impact regarding uncertainties on rock typing and degree of heterogeneities statements [START_REF] Benzagouta | Reservoir characterization: Evaluation for the channel deposits sequence -Upper part using scanning electron microscope (SEM) and mercury injection (MICP): Case of tight reservoirs (North Sea)[END_REF]. Conversely, making use of parameters recorded from the formation evaluation, ambiguity concerns might be improved for sustaining the decrease in rock typing uncertainties [START_REF] Emami | Probabilistic Reservoir-Property Modeling Jointly Constrained by 3D-Seismic Data and Hydraulic-Unit Analysis[END_REF]David Lumley, 2014, Pirrone, M. et al., 2014).

Similar statement is well known in the Lower Shaly -Triassic Sandstone Formation (TAG-I Formation, Algeria), where, the main characteristics controlling rock typing uncertainties, can be owed to rock solids textural and fractional compositional. In addition, contribution is expected from fluid flow filling pores and its properties.

For similar purposes and to clear out various doubts for the case study investigation, conducted tasks can be set as follow:

 Rock typing identification based on integrating, analyzing and synthesizing data from well logs and core analysis.  Rock typing identification related to the volume of shale estimated from gamma-ray (GR), hydraulic units, capillary pressure profile, and saturation height function.  Finding out relation between completed classification process using FZI method and permeability models specified for each rock type.  Anticipating the determination of FZI from well logs application using the empirical model and non-linear regression approaches.  Final objective is to calculated permeability from FZI based on rock typing classification in reservoir sections.

Field description and Well presentation

As part of the Saharan Platform, the Berkine Basin is located in South-Eastern Algeria, between latitude 29 degrees and 33 degrees North and longitude 5 degrees and 9 degrees East (Figure 1). It is limited to the North by the southern border of the Dahar Mole, to the south by the Mole D'Ahara which separates it from the Illizi basin, to the East by the Tunisian -Libyan borders and finally to the West by the structural extension North of the Amguide -El Biode -Hassi Messaoud Mole. The Berkine basin is of intracratonic type with a total area is 102,395 km2 [START_REF] Souadnia | Caractérisation géologique et simulation du réservoir d'hydrocarbure TAGI -HBNS, gisement Hassi Berkine Sud -à l'aide de méthodes géostatistiques[END_REF].

The EXP-01 exploration well has been drilled in the Berkine oil field Basin for assessing the H-C potential of sandstone part within the Triassic Lower Sandstone Clay TAG-I stratigraphic column (Figure 2) . A 15 m core thickness was picked up between 3247 m TVDSS (true vertical depth subsea) and 3262 m TVDSS. A total Core recovery was achieved without any loss (100% recovery). The core height has been found covering the entire reservoir targeted section. The core section has been described from sedimentological and petrographical points (figure 2). Results, based on the stratigraphic log of the core interval and the whole borehole section, indicate a detrital sandstone lithology dominance, which may be split up into different facies deposits according to Turner, P. et al., 2001 (figure2). From sedimentological point of view, the Berkine basin deposits have been found of braided to meandering fluvial environmental deposits [START_REF] Turner | Sequence stratigraphy and sedimentology of the late Triassic TAG-I (Blocks 401/402, Berkine Basin, Algeria)[END_REF].

Reservoir characteristics have also been found of relatively up to good with porosity varying from 7 to 20 % and the permeability from 1 millidarcies to 850 millidarcies (mD). The PVT (Pressure, Volume, and Temperature) analysis has indicated light oil, with a gravity of 40.4 API [START_REF] Souadnia | Caractérisation géologique et simulation du réservoir d'hydrocarbure TAGI -HBNS, gisement Hassi Berkine Sud -à l'aide de méthodes géostatistiques[END_REF].

Core Samples porosity and permeability measurements

42 conventional core measurements have been selected from the reservoir interval (essentially the Net pay sections) in the first exploration borehole. Porosity and the permeability are targeted in the assessment purpose. Six special core measurements with mercury intrusion-porosimetry have been selected to define capillary pressure (Pc). The experiments have been realized in the laboratory. Prior to petrophysical characteristics measurements, the core plugs underwent the following laboratory procedures, at ambient conditions.

-Cleaning procedure -Helium derived porosity -Klinkenberg corrected gas permeability

Cleaning and drying

The preferred Routine core analysis (RCA) method for cleaning of plugs has been the hot Soxhlet reflux cleaning with methanol and toluene as the most used cleaning liquids, followed by chloroform/methanol and finally methanol, Extended cleaning for 2-4 weeks are often necessary depending the core permeability. Drying is normally done at 105-110 °C to remove adsorbed humidity and obtain the most accurate porosity and grain density figures.

Helium porosity determination

Porosity was determined by helium injection using a Boyle's law porosimeter. The bulk volume was measured using the immersion in the Mercury technique. The method used for porosity calculation is based on consolidated rocks. The method was based on some cylindrical plugs allowing the volume calculation (bulk volume) (irregular shapes are not accurate): length and diameter have to be measured accurately.

We can determine the solid volume or the pore volume by saturation subsequent to air evacuation using a vacuum pump. Total Saturation of the sample is accomplished by a fluid of known density. The sample is weighted (Ww). Then Sample is dried (furnace). The difference in weight between saturated and dry samples can give us the pore volume according to fluid density (generally when it consists of water: water density = 1 g/cm3. The porosity is expressed as:

𝑃𝑜𝑟𝑜𝑠𝑖𝑡𝑦 (∅) = 𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑃𝑜𝑟𝑒 (𝑉 𝑝 ) 𝑇𝑜𝑡𝑎𝑙 𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑟𝑜𝑐𝑘 (𝑉 𝑏 )
* 100

(1)

Klinkenberg corrected gas permeability

The general measurement of permeability (K) is based on the type of fluid, its viscosity, pressure difference between the inlet and outlet, sample section and its length. The permeability measurement has been taken in the company laboratory. It has been measured by using core cylindrical samples according to the process: A fluid of known viscosity (μ) is pumped through a rock sample of known cross-sectional (A) area and length (L). The pressure drop across the sample is measured through pressure gauges (𝛥𝑝 = 𝑃 𝑖𝑛𝑙𝑒𝑡 -𝑃 𝑜𝑢𝑡𝑙𝑒𝑡 ).

The Darcy's Law as formulated by Muskat and Botset is as follows [START_REF] Muskat | FLOW OF GAS THROUGH POROUS MATERIALS[END_REF]:

𝑄 = 𝑘 * (𝑃 1 -𝑃 2 ) * 𝐴 𝜇 * 𝐿 (2)
Where: 𝑄: Rate of flow (cm3/s) 𝑘: Permeability (Darcy) (𝑃 1 -𝑃 2 ) : Pressure drop across the sample (atmosphere) 𝐴: Cross-sectional area of the sample (cm2) 𝜇: Viscosity of fluid (cP) 𝐿: Length of the sample (cm) Nothing that, in the form shown above, we assume that the equation ( 2) is occurring without any chemical reaction between the fluid and the rock, with only one fluid phase (after cleaning procedure).

The permeability measurement procedure can be illustrated by the schematic diagram below (Figure 3):

Rock Typing; Identification and assignment

In reservoir engineering, rock classification can be made on the basis of hydraulic unit identification, planned for fluid circulation ability and determination (Attia, M. [START_REF] Attia | Identification of Barriers and Productive Zones Using Reservoir Characterization[END_REF]. These hydraulic units (HU) are used for modeling parameters such as permeability (K) [START_REF] Mahjour | Identification of Flow-units using Methods of Testerman Statistical Zonation, Flow Zone Index, and Cluster Analysis in Tabnaak Gas Field[END_REF] in order to optimize simulation time. Rock typing is determined on the basis of reservoir petrophysical properties, porositypermeability cross plot, capillary pressure curves, in addition to water saturation height function profiles. Generally, the determination of these parameters will help to define different rock classes and their contribution potential towards predicted recovery. Conducting similar research consists on making use of data from different sources (well logs and core data) to support identification of the different rock types. As mentioned previously, the static behavior (Lithology, rock type and physical properties) can be gathered to dynamic behavior (petrophysical parameters relationship and capillary pressure effect). In that principle, defined rock types must be calibrate in terms of the following defined setting:

 Lithofacies: the same type of rock in terms of lithology.  Petrofacies: the process is based on the classification of cores data into sets having the same hydraulic unit (FZI method), the same pore size and similar capillary pressure profile.

Application for the case study: Results and Discussion

In the case study, exploitation of cores, coming from the first explored borehole in the Hassi -Berkine oil field (Algeria), has been used for rock typing identification. These cores have been characterized on the basis of porosity and permeability properties. These factors are considered as the main indicators for the reservoir classification process. Before going through this process, a plotting permeability versus porosity relation is required and becomes essential. According to permeability porosity distribution, and regarding the porosity evolution, two groups of samples distribution were set up main (normal) samples and anomalous samples. In addition, a relation between permeability and porosity was also established (Figure 4).

Permeability Porosity relationship

Permeability versus porosity relation, recorded from core analysis, presents a nonuniform cloud over which a representative mathematical model is privileged. This predicted model can be set on the basis of a best-provided correlation coefficient. Providentially, the rock type classification procedure begins by removing core results located out of the main set of points. Permeability versus porosity recorded outcomes from core analysis indicates the best fit line, crossing a group with a correlation coefficient of 0.62 (Figure 4). This low value of the correlation coefficient provides a considerable margin of error. All calculations, depending directly or indirectly on absolute permeability, will be overestimated.

Therefore the numerical simulation model does not represent the real field performance. In that purpose, the application of the HFU parameter becomes necessary to predict the various reservoir rock types (RRT) and the degree of reservoir heterogeneities. With reference to parameters control, this request will possibly improve the absolute permeability calculation and subsequently extended to the uncored sections, thus, this prediction will be used to decrease the uncertainties surrounding the uncored zones.

Figure 4: A cross plot indicating permeability versus porosity where heterogeneity is illustrated through a predetermined interval of distribution

Data availability and Quality Control (QC)

To carry out a complete study on reservoir rock types classification in the cored and uncored wells, a data preparation must be carried out, and presented as follows;

 Core data in cored wells, and well logs data in all wells should be prepared and quality controlled.  Quality insurance of core data should be necessary; all cores destroyed during the sampling should be removed.  Porosity estimation from well logs should be calibrated to that calculated from cores with consideration of the overburden phenomenon.  Core data depth correction must be adjusted.

Rock Typing

According to [START_REF] Chehrazi | Pore-facies as a tool for incorporation of small-scale dynamic information in integrated reservoir studies[END_REF], reservoir rock typing is a process of classifying reservoir rocks into distinct units. From a geological point of view, it is characterized by similar geological conditions deposited in the same sedimentary environment and undergone through similar diagenetic alterations.

From the reservoir engineering point of view, it is characterized by identical fluid flow properties. Based on these definitions, given rock type can be imprinted by a unique permeability -porosity relationship, capillary pressure profile and saturation height functions above free water level (HFWL).

In the case study, reservoir rock types identification is the process by which rocks are regrouped in specific sets and are calibrated in terms of lithofacies and petrofacies. In the case study, two principal steps have been considered to define reservoir rock types:

Lithofacies identification

Lithofacies determination is derived from the description of core and cuttings obtained during the drilling phase. The different lithological units are grouped, as mentioned previously, to similarities in rock composition, texture, and sedimentary structures. Therefore, each lithofacies should be associated with a specific Rock type.

According to the carried out description of the cores and the cutting, the whole material consists on detrital deposits. Therefore, shaliness or clay content parameter constitutes a basic parameter to be used for splitting up between the different facies. The availability of Gamma ray records is a valid tool for this function [START_REF] Turner | Sequence stratigraphy and sedimentology of the late Triassic TAG-I (Blocks 401/402, Berkine Basin, Algeria)[END_REF][START_REF] Benzagouta | Reservoir Heterogeneities, in Fractured Fluvial Reservoirs of the Buchan Oilfield (Central North Sea)[END_REF]. For that purpose, the gamma-ray log has been used, as the main source for lithofacies identification and classification, matching the defined lithofacies from core description and cutting (Table 1). Based on this perceptive concept, five lithofacies were defined in Hassi Berkine Oil Field. Two defined types of lithofacies: organic-rich shale and shales are considered as non-reservoir with regards to the others. Plotting core permeability versus porosity for defined reservoir subunits shows that three lithofacies could be considered as probable reservoir efficient facies; Shaly sandstone, sandstone and clean sandstone (Figure 5).

Commun lithological description Lithofacies Codes

Non Reservoir Dark clay rich in organic matter content Organic-rich Shales 1 Greenish clay deposits Shales

Reservoir (net pay)

Heterogeneous lithic facies alternating with fine to very fine sand and clay with some pebbles including coal fragments and some mud-clasts.

Shaly Sandstone 2

fine to medium sandstone Sandstone 3 Medium to coarse clean sandstone Clean Sandstone 4

Table 1: Table showing the main lithofacies characteristics in the considered reservoir according to [START_REF] Asquith | Basic Well Logging Analysis for Geologists[END_REF][START_REF] Turner | Sequence stratigraphy and sedimentology of the late Triassic TAG-I (Blocks 401/402, Berkine Basin, Algeria)[END_REF][START_REF] Benzagouta | Reservoir Heterogeneities, in Fractured Fluvial Reservoirs of the Buchan Oilfield (Central North Sea)[END_REF] Figure 5: Well log interpretation based on GR classification indicating the location of core data in reservoir zones

Petrofacies determination

In this case study, [START_REF] Amaefule | Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic (Flow) Units and Predict Permeability in Uncored Intervals/Wells[END_REF] method was applied to identify hydraulic units. This latter parameter could be presented by a unique permeabilityporosity relationship. Results are indicated in Figure 6. This method deals with the rock quality index (RQI) versus normal porosity (∅z). The calculation of these parameters has been achieved graphically based on the unit slope. As a result, six hydraulic units revealing six dynamic curve behaviors were obtained (Figure 6a).

For each hydraulic unit, permeability factor was obtained from 𝐹𝑍𝐼 𝑚𝑒𝑎𝑛 and effective porosity using the equation below (equation 3) [START_REF] Enaworu | PERMEABILITY PREDICTION IN WELLS USING FLOW ZONE INDICATOR (FZI)[END_REF]. Consequently, various rock types are laid down (Figure 6b).

𝑘 = 1014 * 𝐹𝑍𝐼 𝑚𝑒𝑎𝑛 2 [ ∅ 𝑒 3 (1 -∅ 𝑒 ) 2 ] (3)
Then, the units were modeled by a linear, logarithmic, exponential and power-law in order to determine the best representative equation corresponding to each hydraulic unit (Table 2, Figure 6c). The choice of a mathematical model, representing the same set of points, is coupled to the correlation coefficient. This correlative coefficient is used to measure the strength of the relationship between the two essential petrophysical parameters: porosity and permeability. Figure 6d indicates, that the best correlation between the cited type of modeled and actual cores measurements permeability, with a high degree of accuracy, is set through the correlation coefficient value of 0.97.

In addition to the identification of the hydraulic units, six collected samples, from the reservoir area (net pay section or probable efficient section), were used to calculate the capillary pressure (Pc) by the Mercury Injection Capillary Pressure experiment (MICP). The principle is to evaluate the capillary behavior of these samples as a function of defined hydraulic units. Figure 7a is a graphical analysis of capillary pressure versus saturation: each capillary pressure value corresponds to a respective hydraulic unit. It is found that rock types RT-5 and RT-6 have the same initial water saturation (Figure 7b). This can be a support to set together rock types RT-5 and RT-6 in the same rock type, sustaining a consistent classification, in terms of hydraulic units and capillary pressure profiles. Consequently, the petrophysical rock types will be summarized in five rock types RT-1, RT-2, RT-3, RT-4 and RT-5bis (RT-5 and RT-6).

According to the outcomes, from the figures 6a and 6b, the presence of more than one hydraulic unit and dissimilarities in the capillary pressure profiles can be ascribed to the heterogeneity of depositional environment change or lithological distribution variation. For supporting this hypothesis, pore size distribution has been calculated from capillary pressure and presented in figure 8.

Graphical analysis shows that pore throat is of mostly macroporous type, with a pore throat radius fluctuating between 2.5 and 10 microns meter, with minor variations of mesoporous (0.5 -2.5 microns meter) and microporous category (0.2 -0.5 microns meter).

Accordingly, the reservoir rock quality type can be determined according to factors such as porosity, permeability, hydraulic unites, capillary pressure and pore throat radius. The determination of the values of this last parameter can be an effective device to assess the heterogeneity degree. Based on pore size distribution profiles, reservoir facies could be considered as relatively homogenous along the field sections. The presence of more than one hydraulic unit can be related to variation in lithological facies distribution.

In the case study and as results, Hassi Berkine oil field reservoir rock types (RRT) have been classified into five lithofacies and six petrofacies, the results are shown in Table 3.

Permeability Estimation for Non-Cored Section

According to the flow zone indicator method applying for rock typing identification, the permeability models for each rock type has been established and defined in the cored section [START_REF] Enaworu | PERMEABILITY PREDICTION IN WELLS USING FLOW ZONE INDICATOR (FZI)[END_REF]. In order to calculate the permeability for the non-cored section, the determination of FZI should be necessary. For that reason, nonlinear regression methods have been carried out to calculate FZI in the non-cored section.

For accomplishing the precedent purpose, well logs data should be used for analysis and interpretation. It will provide an approach allowing non-cored section classification into reservoir rock types. Thus, for such setting FZI and Permeability models could be applied.

In the case study, the volume of shale (𝑉 𝑠ℎ ) determination has subdivided the reservoir zone into four sub-zones. The sub-zones have been defined as Sand, Shaly-Sand, Sandy-Shale, and Shale (Table 4).

Cores are located in the sandstone (unite 4) and shaly-sand (unite 3) zones (Figure 5, Table 4).

According to Tanmay [START_REF] Chandra | Permeability estimation using flow zone indicator from Well log data[END_REF], FZI can be calculated from the combined use of well log data such as Gamma-ray (GR), NPHI, RHOB, and Sonic (DT) (Equation 4): In this study, our approach is focusing on the determination of the normalized FZI from combined use of well log data such as Gamma-ray (GR), NPHI, RHOB, and Sonic (DT). Based on this understanding, empirical modeling (EM) was applied to create models based on the experimental data to predict the normalized FZI model calculated from well logs and accordingly used in permeability calculation through the application of the reservoir rock typing process. In order to achieve this objective, several models were produced on the basis of the combination of mathematical functions such as; linear, exponential, logarithmic, power and rational functions using conventional well logs such as; Gammaray, NPHI, RHOB, and DT. The best mathematical model must be related to the optimal subject function and characterized by its specific logs. The objective function or the optimized goal of the proposed model consists essentially to minimize the quadratic error. This later will be between the calculated and the observed normalized FZI as mentioned in the equation 5:

𝑂𝑏𝑗 𝑓𝑢𝑛𝑐 = 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 (∑(𝐹𝑍𝐼 𝑜𝑏𝑠 * -𝐹𝑍𝐼 𝑐𝑎𝑙𝑐 * ) 2 𝑛 𝑖=1 ) (5)
Where:

𝑂𝑏𝑗 𝑓𝑢𝑛𝑐 : The objective function

Where 𝐹𝑍𝐼, 𝐹𝑍𝐼 𝑚𝑖𝑛 and 𝐹𝑍𝐼 𝑚𝑎𝑥 are considered from core data.

We note that 𝐹𝑍𝐼 * is constrained by the rock lithofacies (Shaly-Sandstone and Sandstone & clean Sandstone subzones), this means that each lithofacies has a specific mathematical model.

In this investigation, in order to solve the nonlinear optimization problem and compute the optimum model parameters according to the chosen subject function, the Generalized Reduced Gradient (GRG) method was performed [START_REF]Numerical optimization strategies for springback compensation in sheet metal forming[END_REF]. The mathematical models of normalized FZI (𝐹𝑍𝐼 𝑐𝑎𝑙𝑐 * ) for shaly-sandstone and sandstone & clean sandstone subzones were developed. They were performed on the basis of several scenarios carried out on the observed normalized FZI coming from cores and normalized FZI estimated from normalized well logs, RHOB* and DT*. NPHI* has been used as a parameter, but it has not led to good results. Thus, the mathematical models deduced for the two subzones are correspondingly: Where 𝑅𝐻𝑂𝐵 * and 𝐷𝑇 * are normalized parameters and are calculated by [START_REF] Shier | Well log normalization: Methods and guidelines[END_REF]:

𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑙𝑜𝑔 = 𝑅𝑒𝑎𝑑𝑖𝑛𝑔 𝑉𝑎𝑙𝑢𝑒 -𝑀𝑖𝑛 𝑉𝑎𝑙𝑢𝑒 𝑀𝑎𝑥 𝑣𝑎𝑙𝑢𝑒 -𝑀𝑖𝑛 𝑉𝑎𝑙𝑢𝑒 (9)
Based on the equation 6, the FZI log is calculated from the normalized FZI and stated as follows:

𝐹𝑍𝐼 𝑙𝑜𝑔 = 𝐹𝑍𝐼 𝑐𝑎𝑙𝑐 * * (𝐹𝑍𝐼 𝑚𝑎𝑥 -𝐹𝑍𝐼 𝑚𝑖𝑛 ) + 𝐹𝑍𝐼 𝑚𝑖𝑛 (10)

For the permeability calculation, reservoir rock types were classified on the basis of the FZI values, in which a permeability model was defined for each rock type (Table 5). The results presented in figure 9 indicate a relative fine correlation between calculated and observed parameters (FZI and Permeability), and therefore, these inferred models can be used to calculate the permeability in the non-cored wells.

Conclusion

 Rock typing can be defined as integration and analysis of data from boreholes and core analysis.  Rock typing determination can be due to various factors leading to various modifications.  Lithofacies process is a helpful tool that can be used to link core data to Well log column data.  Identification of hydraulic unit parameters such as HFU and FZI can be a vital compilation between the real cores and petrophysical characteristics.  Capillary pressure and saturation height function are influencing factors regarding reservoir rock typing classification.  Pore size distribution (PSD) could be introduced to identify the reservoir degree of homogeneity or heterogeneity.  Uncertainties on data analysis with various obtained mathematical curves can be involved and useful to find out and support the correlation between hydraulic units issued from core data and well logs.  The use of the classification process can be main concern for the permeability determination and its anticipation for each rock type.  Similar investigation on reservoir characterization steps can be applied for other non-cored wells especially for boreholes set in the same structure and having broad-spectrum characteristics.

Conflict of interests

We attest and witness that there is no conflict of interest concerning the manuscript.

Figure 1 :Figure 2 :

 12 Figure 1: The localization of the study area and Well position[START_REF] Souadnia | Caractérisation géologique et simulation du réservoir d'hydrocarbure TAGI -HBNS, gisement Hassi Berkine Sud -à l'aide de méthodes géostatistiques[END_REF]

Figure 3 :

 3 Figure3: General permeability measurement scheme According to Darcy Law[START_REF] Zand | A Simple Laboratory Experiment for the Measurement of Single Phase[END_REF] Detailed information on the purpose of these procedures of laboratory measurements and various steps, for the core use, were figured out from laboratory core analysis and guides[START_REF] Mcphee | Core analysis : a best practice guide[END_REF][START_REF] Lyons | Standard Handbook of Petroleum and Natural Gas Engineering, Third Edition[END_REF].

Figure 6 :

 6 Figure 6: Permeability vs. porosity with the obtained different curves and correlation coefficient:Different clusters have come out with various hydraulic units leading to different rock typing[START_REF] Amaefule | Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic (Flow) Units and Predict Permeability in Uncored Intervals/Wells[END_REF]

Figure 9 :

 9 Figure 9: FZI and Permeability profiles calculated from well logs data

Table 2 :

 2 Table summarizing the classification of rocks based on FZI methodFigure 7: Capillary pressure (Pc) and Saturation height function (HFWL) profiles for each reservoir rock type (RRT) Figure 8: Pore size distribution deduced from capillary pressure (MICP method)

	Reservoir/Non Reservoir	Lithofacies		Petrofacies
	Non Reservoir	Organic rich shales	RT0	Non Reservoir
		Shales
	Reservoir	Shaly-Sandstone	RT-1	FZI < 0.9205
			RT-2	0.9205 <FZI<1.8033
		Sandstone	RT-3	1.8033 < FZI < 2.7785
			RT-4	2.7785 < FZI < 4.5697
		Clean Sandstone	RT-5bis	FZI > 4.5697

Table 3 :

 3 Reservoir rock types (RRT) Classification

Table 4 :

 4 Clay evolution (𝑉 𝑠ℎ ) in the reservoir based on GR readings

	𝐹𝑍𝐼 = 𝑓(𝐺𝑅, 𝑁𝑃𝐻𝐼, 𝑅𝐻𝑂𝐵, 𝐷𝑇)	(4)

Table 5 :

 5 Table summarizing the permeability models for each rock type

	Rock Types	FZI Intervals	Function	Modelled Permeability	Correlation Coefficient
	RT-1	FZI < 0.9205	Power	1676.2 * ∅ 3.143	0.755
	RT-2	0.9205 <FZI<1.8033	Exponential	0.2117 * 𝑒𝑥𝑝 26.864 * ∅	0.9767
	RT-3	1.8033 < FZI < 2.7785	Power	19378 * ∅ 3.5768	0.6863
	RT-4	2.7785 < FZI < 4.5697	Exponential	1.3455 * 𝑒𝑥𝑝 24.801 * ∅	0.9025
	RT-5bis	4.5697 < FZI < 6.7378 FZI > 6.7378.	Exponential Logarithmic	6.3244 * 𝑒𝑥𝑝 21.51 * ∅ 826.89 * ln(∅) + 2093.3	0.9158 0.9837