Amelie Cavelan
email: amelie.cavelan@univ-lorraine.frhighlights

Mohammed Boussafir

and mesoporosity during artificial thermal maturation of an organic-rich mudstone?

Keywords: Artificial thermal maturation, rock fabric, mesoporosity, mudstone

Does grain size influence hydrocarbons generation

Introduction

A further understanding of the variation of the porosity of mudstones during maturation is necessary to predict the spots favorable for successful exploitation of shale oil and gas [START_REF] Clarkson | Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion[END_REF][START_REF] Katz | Organic porosity: a geochemist's view of the current state of understanding[END_REF][START_REF] Ross | The importance of shale composition and pore structure upon gas storage potential of shale gas reservoirs[END_REF]. To better explain how OM composition and thermal maturity affect organic-rich mudstone porosity, laboratory thermal maturations were increasingly used these last years since they ensure to obtain rocks of different thermal maturity from the same initial immature sample [START_REF] Cao | Pore formation and evolution of organicrich shale during the entire hydrocarbon generation process: Examination of artificially and naturally matured samples[END_REF]Cavelan et al., 2019aCavelan et al., , 2020a;;[START_REF] Chen | Evolution of nanoporosity in organic-rich shales during thermal maturation[END_REF][START_REF] Guo | Evolution of organic matter and nanometer-scale pores in an artificially matured shale undergoing two distinct types of pyrolysis: A study of the Yanchang Shale with Type II kerogen[END_REF][START_REF] Han | Pore structure evolution of lacustrine shales containing type I organic matter from the upper cretaceous Qingshankou formation, Songliao Basin, China: A study of artificial samples from hydrous pyrolysis experiments[END_REF][START_REF] Ko | Pore and pore network evolution of Upper Cretaceous Boquillas (Eagle Ford-equivalent) mudrocks: Results from gold tube pyrolysis experiments[END_REF][START_REF] Ko | Pore-types and porenetwork evolution in Upper Devonian-Lower Mississippian Woodford and Mississippian Barnett mudstones: Insights from laboratory thermal maturation and organic petrology[END_REF][START_REF] Song | Petroleum Generation, Retention, and Expulsion in Lacustrine Shales Using an Artificial Thermal Maturation Approach: Implications for the In-Situ Conversion of Shale Oil[END_REF][START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF][START_REF] Tan | Pore Evolution of Lacustrine Organic-Rich Shales: Insights from Thermal Simulation Experiments[END_REF][START_REF] Wang | Understanding pore evolution in a lacustrine calcareous shale reservoir in the oil window by pyrolyzing artificial samples in a semi-closed system[END_REF][START_REF] Wang | Gas Adsorption Characterization of Pore Structure of Organic-rich Shale: Insights into Contribution of Organic Matter to Shale Pore Network[END_REF][START_REF] Xu | Organic-induced nanoscale pore structure and adsorption capacity variability during artificial thermal maturation: Pyrolysis study of the Mesoproterozoic Xiamaling marine shale from Zhangjiakou, Hebei, China[END_REF]. Initially used to calculate kinetic parameters, assess variations of the kerogen structure, mass balance, or quantify oil generation potential, this method significantly improved our knowledge about oil and gas generation mechanisms and hydrocarbon expulsion processes [START_REF] Behar | Artificial maturation of a Type I kerogen in closed system: Mass balance and kinetic modelling[END_REF][START_REF] J O U R N A L P R E -P R O O F Behar | Role of NSO compounds during primary cracking of a Type II kerogen and a Type III lignite[END_REF][START_REF] Behar | Comparison of artificial maturation of lignite in hydrous and nonhydrous conditions[END_REF][START_REF] Behar | Thermal cracking of kerogen in open and closed systems: determination of kinetic parameters and stoichiometric coefficients for oil and gas generation[END_REF][START_REF] Behar | Experimental simulation in a confined system and kinetic modelling of kerogen and oil cracking[END_REF][START_REF] Behar | Représentation chimique de la structure des kérogènes et des asphaltènes en fonction de leur origine et de leur degré d'évolution[END_REF][START_REF] He | Hydrocarbon generation-retention-expulsion mechanism and shale oil producibility of the permian lucaogou shale in the Junggar Basin as simulated by semi-open pyrolysis experiments[END_REF][START_REF] Landais | Are time and temperature the only constraints to the simulation of organic matter maturation?[END_REF][START_REF] Lewan | Experiments on the role of water in petroleum formation[END_REF][START_REF] Michels | Effects of pressure on organic matter maturation during confined pyrolysis of Woodford kerogen[END_REF][START_REF] Monthioux | Comparison between natural and artificial maturation series of humic coals from the Mahakam delta, Indonesia[END_REF] and their relation with the formation of pores and the storage capacity of shale reservoirs (Cavelan et al., 2019a; J o u r n a l P r e -p r o o f [START_REF] Chen | Evolution of nanoporosity in organic-rich shales during thermal maturation[END_REF][START_REF] Guo | Evolution of organic matter and nanometer-scale pores in an artificially matured shale undergoing two distinct types of pyrolysis: A study of the Yanchang Shale with Type II kerogen[END_REF][START_REF] Han | Pore structure evolution of lacustrine shales containing type I organic matter from the upper cretaceous Qingshankou formation, Songliao Basin, China: A study of artificial samples from hydrous pyrolysis experiments[END_REF][START_REF] Ko | Pore-types and porenetwork evolution in Upper Devonian-Lower Mississippian Woodford and Mississippian Barnett mudstones: Insights from laboratory thermal maturation and organic petrology[END_REF][START_REF] Song | Petroleum Generation, Retention, and Expulsion in Lacustrine Shales Using an Artificial Thermal Maturation Approach: Implications for the In-Situ Conversion of Shale Oil[END_REF][START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF][START_REF] Wang | Understanding pore evolution in a lacustrine calcareous shale reservoir in the oil window by pyrolyzing artificial samples in a semi-closed system[END_REF][START_REF] Wang | Gas Adsorption Characterization of Pore Structure of Organic-rich Shale: Insights into Contribution of Organic Matter to Shale Pore Network[END_REF][START_REF] Xu | Organic-induced nanoscale pore structure and adsorption capacity variability during artificial thermal maturation: Pyrolysis study of the Mesoproterozoic Xiamaling marine shale from Zhangjiakou, Hebei, China[END_REF]. Today several conventional and MBE (material balance equation) models used to estimate the gas sorption capacity of kerogen at different thermal maturity are still partly based on the knowledge acquired during these experiments [START_REF] Alafnan | Petrophysics of Kerogens Based on Realistic Structures[END_REF][START_REF] Alafnan | Effect of Kerogen Thermal Maturity on Methane Adsorption Capacity: A Molecular Modeling Approach[END_REF][START_REF] Ambrose | Shale gas-inplace calculations part I: new pore-scale considerations[END_REF][START_REF] Ungerer | Molecular Modeling of the Volumetric and Thermodynamic Properties of Kerogen: Influence of Organic Type and Maturity[END_REF]. However, the experimental conditions (open, semi-confined, confined, the chosen duration/temperature, hydrous/anhydrous conditions, rock fabric) chosen for these maturations is known to influence OM thermal degradation processes [START_REF] Behar | Comparison of artificial maturation of lignite in hydrous and nonhydrous conditions[END_REF]Cavelan et al., 2020a;[START_REF] İnan | Gaseous hydrocarbons generated during pyrolysis of petroleum source rocks using unconventional grain-size: implications for natural gas composition[END_REF][START_REF] Landais | Are time and temperature the only constraints to the simulation of organic matter maturation?[END_REF][START_REF] Michels | Effects of effluents and water pressure on oil generation during confined pyrolysis and high-pressure hydrous pyrolysis[END_REF][START_REF] Monthioux | Expected mechanisms in nature and in confined-system pyrolysis[END_REF][START_REF] Shao | Experimental investigation of the role of rock fabric in gas generation and expulsion during thermal maturation: Anhydrous closed-system pyrolysis of a bitumen-rich Eagle Ford Shale[END_REF][START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF][START_REF] Suárez-Ruiz | Influence of rock particle size on the artificial thermal evolution of kerogen. A petrographic and geochemical study[END_REF]. The mineral phase, and especially clay minerals, is known to act as a support medium of the OM and a catalyzer during its thermal transformations [START_REF] Tannenbaum | Role of minerals in thermal alteration of organic matter-II: A material balance[END_REF][START_REF] Tannenbaum | Role of minerals in the thermal alteration of organic matter-I: Generation of gases and condensates under dry condition[END_REF][START_REF] Tissot | Petroleum formation and occurrence[END_REF].

The relative distribution of OM and minerals in rocks, the size of the OM domains, and the degree of confinement of the system may influence oil and gas generation during burial [START_REF] Michels | Effects of effluents and water pressure on oil generation during confined pyrolysis and high-pressure hydrous pyrolysis[END_REF][START_REF] Michels | Effects of pressure on organic matter maturation during confined pyrolysis of Woodford kerogen[END_REF][START_REF] Suárez-Ruiz | Influence of rock particle size on the artificial thermal evolution of kerogen. A petrographic and geochemical study[END_REF]. In particular, during artificial maturation, OM diagenetic processes can be affected by the physical and morphological properties of the pyrolyzed sample [START_REF] İnan | Gaseous hydrocarbons generated during pyrolysis of petroleum source rocks using unconventional grain-size: implications for natural gas composition[END_REF]Shao et al., 2018;[START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF][START_REF] Suárez-Ruiz | Influence of rock particle size on the artificial thermal evolution of kerogen. A petrographic and geochemical study[END_REF]. Probably due to better confinement achieved in core samples, these works observed different compositions and contents of hydrocarbons generated by the OM after the maturation of cores and powdered samples. In particular, they showed that the use of core samples may favor the expulsion of oil [START_REF] Shao | Experimental investigation of the role of rock fabric in gas generation and expulsion during thermal maturation: Anhydrous closed-system pyrolysis of a bitumen-rich Eagle Ford Shale[END_REF][START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF]. Considering the effect that the thermal degradation of OM can have on the development of mudstone porosity (Cavelan et al., 2020b;[START_REF] Chen | Evolution of nanoporosity in organic-rich shales during thermal maturation[END_REF][START_REF] Curtis | Development of organic porosity in the Woodford Shale with increasing thermal maturity[END_REF][START_REF] Fishman | The nature of porosity in organic-rich mudstones of the Upper Jurassic Kimmeridge Clay Formation, North Sea, offshore United Kingdom[END_REF][START_REF] Guo | Evolution of organic matter and nanometer-scale pores in an artificially matured shale undergoing two distinct types of pyrolysis: A study of the Yanchang Shale with Type II kerogen[END_REF][START_REF] Hackley | Application of organic petrography in North American shale petroleum systems: A review[END_REF][START_REF] Han | Pore structure evolution of lacustrine shales containing type I organic matter from the upper cretaceous Qingshankou formation, Songliao Basin, China: A study of artificial samples from hydrous pyrolysis experiments[END_REF][START_REF] Katz | Organic porosity: a geochemist's view of the current state of understanding[END_REF][START_REF] Klaver | BIB-SEM characterization of pore space morphology and distribution in postmature to overmature samples from the Haynesville and Bossier Shales[END_REF][START_REF] Ko | Pore-types and porenetwork evolution in Upper Devonian-Lower Mississippian Woodford and Mississippian Barnett mudstones: Insights from laboratory thermal maturation and organic petrology[END_REF][START_REF] Löhr | Is organic pore development in gas shales influenced by the primary porosity and structure of thermally immature organic matter?[END_REF][START_REF] Loucks | Morphology, genesis, and distribution of nanometer-scale pores in siliceous mudstones of the Mississippian Barnett Shale[END_REF][START_REF] Mastalerz | Porosity of Devonian and Mississippian New Albany Shale across a maturation gradient: Insights from organic petrology, gas adsorption, and mercury intrusionGeohorizon[END_REF][START_REF] J O U R N A L P R E -P R O O F Milliken | SEM petrography of eastern Mediterranean sapropels: Analogue data for assessing organic matter in oil and gas shales[END_REF][START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF][START_REF] Song | Petroleum Generation, Retention, and Expulsion in Lacustrine Shales Using an Artificial Thermal Maturation Approach: Implications for the In-Situ Conversion of Shale Oil[END_REF][START_REF] Wang | Understanding pore evolution in a lacustrine calcareous shale reservoir in the oil window by pyrolyzing artificial samples in a semi-closed system[END_REF][START_REF] Wang | Gas Adsorption Characterization of Pore Structure of Organic-rich Shale: Insights into Contribution of Organic Matter to Shale Pore Network[END_REF][START_REF] Xu | Organic-induced nanoscale pore structure and adsorption capacity variability during artificial thermal maturation: Pyrolysis study of the Mesoproterozoic Xiamaling marine shale from Zhangjiakou, Hebei, China[END_REF], we can suppose that the use of different rock particle sizes during artificial maturation may affect the thermal degradation of the OM, leading to different OM-hosted pore development. Indeed, [START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF] Recent computation works for estimating the gas storage capacity of kerogen in shales are based on porosity parameters and in particular on Langmuir isotherms and Langmuir volumes which depend on the kerogen composition but also the porosity formed during maturation [START_REF] Alafnan | Petrophysics of Kerogens Based on Realistic Structures[END_REF][START_REF] Alafnan | Effect of Kerogen Thermal Maturity on Methane Adsorption Capacity: A Molecular Modeling Approach[END_REF][START_REF] Ambrose | Shale gas-inplace calculations part I: new pore-scale considerations[END_REF][START_REF] He | Hydrocarbon generation-retention-expulsion mechanism and shale oil producibility of the permian lucaogou shale in the Junggar Basin as simulated by semi-open pyrolysis experiments[END_REF][START_REF] Memon | An experimental study of gas sorption, adsorbed, and sorbed porosity, and their impact on shale gas-in-place calculations[END_REF][START_REF] Ungerer | Molecular Modeling of the Volumetric and Thermodynamic Properties of Kerogen: Influence of Organic Type and Maturity[END_REF]. The pore size distributions of shales may also largely influence gas-in-place calculation [START_REF] Tolbert | Quantifying Pore Size Distribution Effect on Gas in Place and Recovery Using SLD-PR EOS for Multiple-Components Shale Gas Reservoir[END_REF].

Differences in specific surface area, pore-volume, or pore size distribution obtain between different thermal maturation conditions for the same maturity may, therefore, lead to differences in gas storage capacity and gas in place recovery estimations. Recent numerical works also demonstrated that the properties of the pore network such as the pore radius or the composition of the oils acquired during thermal maturation greatly influence the oil transport in shale micro-fractures and nanopores (Cui, 2019a). For example, the increase in the amount of generated asphaltenes may enhance the permeability of organic pores but reduce the permeability of inorganic pores and micro-fractures (Cui, 2019a), while the radius of pores and fractures controlled the permeability of shales that may impact pre-Darcy flow phenomena mechanisms (Cui, 2019b;[START_REF] Cui | Equivalent anisotropic permeability of shale rocks: Effect of micro-fractures[END_REF]. Therefore, an inaccurate estimation of these parameters (oil composition, oil quantity, pore sizes, etc.) linked to the choice of thermal maturation parameters could, for example, lead to an inaccurate estimation of the flow capacity/properties of the studied shale formation. However, to our knowledge, only [START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF] studied the impact of the rock fabric on the evolution of shale porosity during laboratory thermal maturation. This is insufficient to determine to which extent these results can be applied to all experimental thermal maturation works since they could depend on the maturation system used (semi-confined).

It is, therefore, necessary to better understand how these maturation conditions, which often differ from one study to another, affect the maturation of OM and the resulting porosity. To that end, cores, rock fragments, and powder of an immature mudstone from the Kimmeridge Clay Fm. (Yorkshire, UK) were artificially matured in autoclaves. The composition of the OM, thermal maturity, and J o u r n a l P r e -p r o o f mesopore structure with increasing maturity were determined using nitrogen adsorption and bulk geochemical characterizations (Rock-Eval VI pyrolysis, GC-TCD (gas chromatography-thermal conductivity detector), and GC-MS (gas chromatography-mass spectrometry). The obtained data were compared with naturally matured rocks from the Vaca Muerta Fm. (Cavelan et al., 2020b(Cavelan et al., , 2019b) ) to see which artificial sample series is closest to natural trends.

Methods

Sample

Three cores (4.5 mm*1 cm), rock fragments "RF" (3*3 mm), and powder "PO" (<250µm) were obtained from one immature mudstone from the Kimmeridge Clay Fm. (Yorkshire, UK). The properties of this sample are presented in Tab.1. For each sample type, 1g was firstly oven-dried at 105°C for 12 hours, placed separately in gold tubes before being introduced into high-pressure autoclaves in stainless steel. Each sample was then laboratory-matured under confined and anhydrous conditions at 325°C, 390°C, and 470°C for 72 hours (600 bars, external hydrostatic pressure) with the protocol defined by Cavelan et al. (2019b).

Rock-Eval

To determine the hydrogen index (HI, mg HC./g TOC), Tmax, and total organic carbon (TOC, wt.%) contents of samples after maturation, 60 mg of each sample were crushed into powder and analyzed using new Rock-Eval 6 ® pyrolysis with the same procedure described in (Cavelan et al., 2019a). TOC was calculated from the integration of peaks S1, S2, S3CO, S3CO2, S4CO, and S4CO2 (see Espitalie et al., 1985). The HI represents the amount of pyrolyzed hydrocarbons and was calculated with the following equation (equation 1):

𝐻𝐼 (𝑚𝑔𝐻𝐶/𝑔 𝑇𝑂𝐶) = 𝑆2 * 100 𝑇𝑂𝐶 (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 1)

J o u r n a l P r e -p r o o f

With S2 the amount of hydrocarbons generated by the thermal cracking of the kerogen during the pyrolysis. The Tmax is often used as a maturity indicator and is derived from the Tpic which is the temperature of the peak oil production during pyrolysis (see Espitalie et al., 1985).

Gas chromatography

After maturation, 2 cm3 of gas were sampled with the protocol described in (Cavelan et al., 2019a) and injected in GC-TCD (Perkin Elmer® Clarus 580; Column Supelco® Carboxen 1010 Plot® Capillary: length: 30 m, film thickness: 0.15 µm, internal diameter 0.53 µm). Gas quantification was made using calibration curves and the areas of the peak of each gas on chromatograms. Then, 1g of all artificially matured samples was crushed into powdered and extracted in solvent (dichloromethane: methanol (1:1))

using ultrasonication. The extractible part of the OM (EOM) was then separated on a silica gel chromatography column and the fraction of aromatic (ARO), saturated (SAT), and polar hydrocarbons were then recovered separately by elution with different solvents using the protocol of Cavelan et al. (2020b). Asphaltene contents were obtained by the difference between the mass of the total extract and the mass of the recovered polar hydrocarbons, ARO and SAT. The compounds rich in Nitrogen, Sulfur, or Oxygen (NSO) represents the sum of the asphaltenes and polar hydrocarbons. The molecular composition of ARO and SAT were determined using a GC-MS Trace-GC Ultra (Column: Thermo

Trace-Gold 5 MS, length 60 m, internal diameter 0.25 mm, film thickness 0.25 µm fitted by a mass spectrometer TSQ Quantum XLS. Compounds of interest in the ARO fraction were identified using retention times and mass spectra and quantified using their peak areas on ion-specific chromatograms.

Squalane was the internal standard. Values of equivalent vitrinite reflectance (Rc, %) were then calculated using the methyldibenzothiophene ratios (MDTR), the dimethylphenanthrene ratios (DMPR), and the methylphenanthrene index (MPI-1) with the equation described by [START_REF] Radke | Maturity parameters based on aromatic hydrocarbons: Influence of the organic matter type[END_REF] and [START_REF] Boreham | Alternative calibration of the Methylphenanthrene Index against vitrinite reflectance: Application to maturity measurements on oils and sediments[END_REF]. The mean of these three obtained Rc values is the mean Rc (%). Before and after maturation 60 mg of each sample were powdered and dried before being pyrolyzed using the Rock-Eval® VI method (Espitalié et al., 1985a(Espitalié et al., , 1985b) ) to obtain : (i) the content of total organic carbon J o u r n a l P r e -p r o o f (TOC, in weight % of the rock); (ii) the oil-prone quality of the OM expressed as a Hydrogen Index (HI, mg of HC per g of TOC).

Vintrinite reflectance

To confirm the calculated vitrinite reflectance values obtained from biomarker ratios (Rc), vitrinite reflectance (Rv) was measured on fragments and cores after thermal maturation. Measurements were carried on polished rock sections made from cores and fragments using the preparation and calibration protocol described in (Cavelan et al., 2020b). For each sample, each Rv value is the mean of 50 random measurements.

Nitrogen adsorption measurements

After maturation, the volume, the distribution of the pore sizes (PSD), and the BET surface area were measured using nitrogen adsorption (Quantachrome® NOVA 2200e). Before analysis, each sample was crushed to a particle size < 250 µm before being oven-dried for 12 hours (110°C). The samples were then outgassed for 24h (105°C) directly in the apparatus. The volume of pores accessible to nitrogen was determined using the liquid molar volume of nitrogen adsorbed at the relative pressure (P/Po) = 0.99 with the following equation (equation 2):

𝑃𝑜𝑟𝑒 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑐𝑚3/𝑔) = 𝑎mount of N2 adsorbed at P P0 =0.99 (cm3 STP/g) 647 (N2 liquid volume ratio at 77 K)

(equation 2)

PSDs were calculated on the adsorption isotherms using the method of [START_REF] Barrett | The determination of pore volume and area distributions in porous substances. I. Computations from nitrogen isotherms[END_REF] (often called the "BJH method"). BET surface areas were calculated using the Brunauer, Emmett, and Teller method applied to the adsorption isotherm (BET) between P/Po 0.03 and 0.33 [START_REF] Brunauer | Adsorption of gases in multimolecular layers[END_REF]. Each analysis of BET surface area was done with a minimum of 15 points. Analyses were done at the liquid nitrogen temperature (77K).

Preliminary results obtained by CO2 (Quantachrome -Autosorb-1-MP-6) and N2 (Micromeritics® ASAP -2420) adsorption measurements using the Dubinin-Radushkevich model (DR), Dubunin-Astakhov between P/Po ranging from 0.0001 to 0.01 (adsorption isotherms), and the Horwath-Kawazoe J o u r n a l P r e -p r o o f model between P/Po ranging from 1 to 0.35 showed that the studied samples are predominantly mesoporous. Microporosity (<2 nm) represents less than 8% of the pore volume and specific surface.

Therefore, only mesoporosity (2-50 nm) and part of the macroporosity (> 50 nm) were investigated in this work.

Three replicates of thermal maturations were made from the same sample for each degree of maturity and each type of rock fabric. The presented results show the mean values and standard deviations obtained for these triplicates.

Results and discussion

3.1. Influence of particle size of the pyrolyzed rock on the amount and composition of generated hydrocarbons.

Variation of calculated (Rc) and measured vitrinite reflectance (Rv), HI, Tmax, and concentrations of gas, SAT, NSO, and ARO shows that the three series of samples evolved from the oil to the gas window after heating from 325°C to 470°C (Tab.2, Fig. 1). Whatever the rock fabric of the samples, the early stage of oil production was reached after heating at 325°C (Rc ~0.71%). After maturation at 390 and 470°C, the decrease of the HI, SAT, ARO, and NSO contents, the increase of C1-C5 hydrocarbons gases, and CO2 concentrations indicate the entry in the gas window (Tab.2, Fig. 1). This is well substantiated by the Rc of 1.28% and 2.41% calculated after heating at 390°C and 470°C (Tab.2).

Concerning the extracted OM composition, differences exist between series, especially between the cores and the two other sample types: RF and PO. The few differences observed between the PO and RF are less than 10% and are thus not significant. Even if special care has been taken to sample all series on one small and homogeneous rock, these differences can simply be explained by slight intern variations in the initial composition of the rock or by the slight differences attached to the OM extraction and analyses. These differences are therefore not sufficient to reveal any effect of the sample rock fabric on OM thermal degradation between these series. The experimental thermal maturation of powder and rock fragments gives thus comparable results and can be compared. Moreover, differences are greater J o u r n a l P r e -p r o o f with the core series (Fig. 1). The cores generated 25% less EOM (including NSO, ARO, and SAT concentrations) during the generation of oil (Rc ~0.71%). The relative proportions of ARO, SAT, and NSO are nevertheless similar between RF and PO series (~14%, 18%, and 68% respectively). A similar difference is observed during gas generation. While only a few negligible differences are observed between RF and PO during the condensate wet gas zone (Rc ~1.28%) and the dry gas zone (Rc ~2.41%), the cores generated ~ 30% fewer concentrations of C1-C5 and 50% to 84% fewer concentrations of CO2 (Fig. 1). All series of samples come from the same immature rock (a mudstone rich in OM) treated with the same experimental protocol, these differences are thus probably influenced by the fabric of the laboratory-matured samples. This supports previous results [START_REF] İnan | Gaseous hydrocarbons generated during pyrolysis of petroleum source rocks using unconventional grain-size: implications for natural gas composition[END_REF][START_REF] Shao | Experimental investigation of the role of rock fabric in gas generation and expulsion during thermal maturation: Anhydrous closed-system pyrolysis of a bitumen-rich Eagle Ford Shale[END_REF][START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF][START_REF] Suárez-Ruiz | Influence of rock particle size on the artificial thermal evolution of kerogen. A petrographic and geochemical study[END_REF] and confirmes that the fabric of the rock affects the thermal maturation of the OM and thus the production of oil and gas. These results are in agreement with the observations of [START_REF] Shao | Experimental investigation of the role of rock fabric in gas generation and expulsion during thermal maturation: Anhydrous closed-system pyrolysis of a bitumen-rich Eagle Ford Shale[END_REF], which shows that after thermal maturation under anhydrous conditions of shale samples from the Eagle Ford, the cores generated less C1-C5 and CO2 concentrations than powdered samples. However, contrary to our study, [START_REF] Suárez-Ruiz | Influence of rock particle size on the artificial thermal evolution of kerogen. A petrographic and geochemical study[END_REF] and [START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF] found that the cores had the highest oil yield than the powdered series during artificial thermal maturation. Based on previous works [START_REF] Monthioux | Expected mechanisms in nature and in confined-system pyrolysis[END_REF][START_REF] Monthioux | Comparison between natural and artificial maturation series of humic coals from the Mahakam delta, Indonesia[END_REF], [START_REF] Suárez-Ruiz | Influence of rock particle size on the artificial thermal evolution of kerogen. A petrographic and geochemical study[END_REF] proposed that the increase of the grain size (e.g using cores) favored the retention of the pyrolysis effluents, allowing an increase in (i) the retention time of the compounds in the system; (ii) in the partial pressure of the effluents at the origin of the increase in oil yield during maturation. This assumption is also supported by [START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF]. The thermal maturation of [START_REF] Suárez-Ruiz | Influence of rock particle size on the artificial thermal evolution of kerogen. A petrographic and geochemical study[END_REF] and [START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF], was carried out using an open and semi-closed pyrolysis system respectively, in which the effluents are collected progressively during maturation. In the system of Suárez-Ruiz et al. (1994) no means of confinement is applied on samples, the different degree of confinement of the two sample series during oil generation created by the better ability of the cores to retain oil is therefore very important. In our case, all samples are matured in a confined environment inside sealed gold cells under inert gas and external hydrostatic pressure. This system has two advantages: (i) whatever the rock particle size, the J o u r n a l P r e -p r o o f effluents are trapped in contact with the sample during maturation; (ii) although the means of confinement used in this system was not similar to the lithostatic pressure present in natural basins, it permits to reproduce for all series a certain degree of confinement required to accurately reproduce the thermal degradation of kerogen under laboratory conditions [START_REF] Monthioux | Comparison between natural and artificial maturation series of humic coals from the Mahakam delta, Indonesia[END_REF]. According to these differences of artificial maturation conditions, we can therefore suggest that, in our case, variations in effluents' retention time or in the degree of confinement induced by the sample particle size that is being pyrolyzed are limited. The experimental device used by [START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF] reproduced both hydrostatic and lithostatic pressure conditions. The degree of confinement used is therefore also different from our experiments. This probably explains why we obtained so different results compared with these studies.

However, this highlights the importance of the choice of laboratory thermal maturation conditions.

Results from maturations under different conditions are not strictly comparable with each other or with natural samples. Results from laboratory maturation experiments should therefore be interpreted with caution. Concerning our experiments in anhydrous, closed, and confined conditions, the processes at the origin of the variations of oil and gas generation between our sample series are to be found elsewhere.

We can suggest that the decrease in the sample particle size has: (i) increased the contact surface between the different OM components favoring the OM thermal degradation and increasing the yield of generated hydrocarbons; (ii) increased the contact surface between the OM and clay minerals. Previous works have indeed shown that the presence of Smectite or Illite (abundant in the Kimmeridge clay, Cavelan et al., 2019a) can act as catalysts for OM thermal degradation reactions [START_REF] Huizinga | The role of minerals in the thermal alteration of organic matter-III. Generation of bitumen in laboratory experiments[END_REF][START_REF] Tannenbaum | Role of minerals in thermal alteration of organic matter-II: A material balance[END_REF][START_REF] Tannenbaum | Role of minerals in the thermal alteration of organic matter-I: Generation of gases and condensates under dry condition[END_REF]. They showed that the strong adsorption capacities of these clay minerals and their ability to absorb and retain the heavy polar OM components favor the production of light, gaseous hydrocarbons, and the production of CO2 during the cracking of OM. Increasing the surface contact between OM, all the effluents and the mineral surfaces have thus probably greatly favored the thermal degradation of the OM in the RF and the PO series compared to the cores during our laboratory simulations. This may explain the higher oil yield of the RF and the PO during oil generation but especially their higher C1-C5 and CO2 concentrations during gas generation J o u r n a l P r e -p r o o f (Fig. 1). [START_REF] Shao | Experimental investigation of the role of rock fabric in gas generation and expulsion during thermal maturation: Anhydrous closed-system pyrolysis of a bitumen-rich Eagle Ford Shale[END_REF]found comparable results for the Eagle Ford shales but suggested that the higher production of CO2 was caused by an accelerated carbonates decomposition by the organic acids released by the OM thermal degradation. However, the upper Cretaceous Eagle Ford Shale is composed of carbonates with more than 80 wt% calcite [START_REF] Ko | Pore-types and porenetwork evolution in Upper Devonian-Lower Mississippian Woodford and Mississippian Barnett mudstones: Insights from laboratory thermal maturation and organic petrology[END_REF][START_REF] Shao | Experimental investigation of the role of rock fabric in gas generation and expulsion during thermal maturation: Anhydrous closed-system pyrolysis of a bitumen-rich Eagle Ford Shale[END_REF]. KCF samples contain less than 25% carbonates (Tab. 1, Cavelan et al., 2019a), we can therefore conclude that the measured CO2 is also partly generated from the OM thermal degradation. Comparison with gas-matured Vaca Muerta rocks with similar TOC shows that cores give the closest EOM, ARO, and NSO concentrations during gas generation (Fig. 1). The use of cores during artificial maturation may be more suitable to represent natural OM thermal degradation and that the decrease in the sample particle size may create an artificial enhancement of the OM thermal degradation process in the studied laboratory conditions.

Influence on rock mesoporosity

After heating at 325°C, the volume of pores and specific surface areas of the cores were low (0.26 cm3/100g and 2.0 m²/g respectively) showing a poorly interconnected/low rock porosity. At this maturity stage the PO and the RF exhibit similar specific surface areas, volumes of pores, and PSDs (Fig. 2A) which are mainly composed of ~50% of mesopores and macropores (Fig. 2B). Then, with increasing maturity the specific surface area and the volume of pores increase in all series due to the appearance of pores with a diameter > 10 nm (Fig. 2). This evolution in porosity may mark the formation of OM-hosted pores formed in response to oil and gas production [START_REF] Bernard | Geochemical evolution of organic-rich shales with increasing maturity: A STXM and TEM study of the Posidonia Shale (Lower Toarcian, northern Germany)[END_REF][START_REF] Cao | Pore formation and evolution of organicrich shale during the entire hydrocarbon generation process: Examination of artificially and naturally matured samples[END_REF]Cavelan et al., 2020bCavelan et al., , 2019a;;Chalmers and Bustin, 2008;[START_REF] Curtis | Development of organic porosity in the Woodford Shale with increasing thermal maturity[END_REF][START_REF] Han | Oil retention and porosity evolution in organic-rich shales[END_REF][START_REF] Ko | Pore-types and porenetwork evolution in Upper Devonian-Lower Mississippian Woodford and Mississippian Barnett mudstones: Insights from laboratory thermal maturation and organic petrology[END_REF][START_REF] Ko | Pore and pore network evolution of Upper Cretaceous Boquillas (Eagle Ford-equivalent) mudrocks: Results from gold tube pyrolysis experiments[END_REF][START_REF] Loucks | Morphology, genesis, and distribution of nanometer-scale pores in siliceous mudstones of the Mississippian Barnett Shale[END_REF][START_REF] Wang | Understanding pore evolution in a lacustrine calcareous shale reservoir in the oil window by pyrolyzing artificial samples in a semi-closed system[END_REF]. While a few differences in PSDs, specific surface areas and pore volumes were observed between RF and PO series (Fig. 2), for which similar quantity and composition of generated hydrocarbons were observed (Fig. 1), significant differences in PSD, specific surface areas, pore volumes, EOM, SAR, ARO, NSO, and gas content exist between core series and the other series. During the production of gas, the pore volume of the cores is systematically lower with fewer pores larger than 10 nm (Fig. 2A,B). On the contrary, the cores have larger specific surface areas due to the presence of smaller pores (<10 nm). Cores generated fewer hydrocarbons (liquid and J o u r n a l P r e -p r o o f gaseous) during thermal maturation and therefore, have a lower oil and gas yield (Fig. 1). The lower conversion of organic compounds in cores, related to the larger sample particle size, seems to lead to less or slower pore development. Thus, for similar thermal maturation conditions and maturity, porosity formed in cores is less developed, poorly connected (explaining why pores are inaccessible to nitrogen and not measurable in the oil window).

Comparison with naturally gas-matured Vaca Muerta samples showed once again, that the specific surface area and the PSD of core samples after maturation at 390°C and 470°C are the closest to the natural samples (Fig. 2A,B). However, the pore volumes of Vaca Muerta samples are lower than for RF and PO and significantly higher than for the cores (Fig. 2A). This may be related to the different mineralogy of the Vaca Muerta rocks (dominated by quartz) and KCF (dominated by clay, Cavelan et al., 2019a) which may influence the pore structure and the ability of the rock to preserve its porosity from deformation during maturation.

General implications

Considering these results and previous works [START_REF] Shao | Experimental investigation of the role of rock fabric in gas generation and expulsion during thermal maturation: Anhydrous closed-system pyrolysis of a bitumen-rich Eagle Ford Shale[END_REF][START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF][START_REF] Suárez-Ruiz | Influence of rock particle size on the artificial thermal evolution of kerogen. A petrographic and geochemical study[END_REF], production, retention of hydrocarbons, and the composition of the oil and gas released by an organic-rich mudstone during artificial thermal maturation depend on the rock fabric of the pyrolyzed sample. This generates changes in the evolution of mesoporosity, specific surface areas, and pore volumes. While this confirms the close link existing between the OM thermal degradation reactions and porosity of OM-rich mudstones, it shows the importance of choosing the most appropriate thermal maturation parameters to simulate, as best as possible, the conditions of natural systems. As expected, in the light of our results natural samples seem better represented by core samples, which exhibit the most singular pore volumes and PSD after maturation and the closest features to naturally matured Vaca Muerta rocks. This suggests that confined anhydrous thermal maturation experiments previously carried out on powder and rock fragments may overestimate the amount of hydrocarbons generated and the associated pore volume that develops simultaneously. As observed between these three maturations J o u r n a l P r e -p r o o f experiments and according to previous works, it must nevertheless be kept in mind that these results

show that the magnitude of these differences is influenced by the artificial maturation conditions (degree and means of containment, hydrous pyrolysis…). Indeed, contrary to our observations in confined environments, in semi-confined conditions, the use of powder seems to underestimate the yields of generated oil and C4-C5 gases and to overestimate the yields of C1-C3 hydrocarbons [START_REF] Song | A comprehensive study on the impacts of rock fabric on hydrocarbon generation and pore structure evolution of shale under semi-confined condition[END_REF].

Predicting the differences between experimental maturation on powder, fragments or cores is, therefore, not so easy. In recent years, a great number of works that aimed to better understand the physicalchemical processes affecting the OM in source rocks, the porosity, and the storage capacity of unconventional reservoirs were based on laboratory thermal maturation experiments (e.g. Cavelan et al., 2020bCavelan et al., , 2020cCavelan et al., , 2019a;;[START_REF] Gafurova | Macropores generation in the domanic formation shales: Insights from pyrolysis experiments[END_REF][START_REF] Guo | Evolution of organic matter and nanometer-scale pores in an artificially matured shale undergoing two distinct types of pyrolysis: A study of the Yanchang Shale with Type II kerogen[END_REF][START_REF] Ko | Pore-types and porenetwork evolution in Upper Devonian-Lower Mississippian Woodford and Mississippian Barnett mudstones: Insights from laboratory thermal maturation and organic petrology[END_REF][START_REF] Ko | Pore and pore network evolution of Upper Cretaceous Boquillas (Eagle Ford-equivalent) mudrocks: Results from gold tube pyrolysis experiments[END_REF][START_REF] Song | Petroleum Generation, Retention, and Expulsion in Lacustrine Shales Using an Artificial Thermal Maturation Approach: Implications for the In-Situ Conversion of Shale Oil[END_REF][START_REF] Wang | Understanding pore evolution in a lacustrine calcareous shale reservoir in the oil window by pyrolyzing artificial samples in a semi-closed system[END_REF][START_REF] Wang | Nanoscale pore network evolution of Xiamaling Marine Shale during organic matter maturation by hydrous pyrolysis[END_REF][START_REF] Wu | Oil expulsion in marine shale and its influence on the evolution of nanopores during semi-closed pyrolysis[END_REF][START_REF] Xu | Organic-induced nanoscale pore structure and adsorption capacity variability during artificial thermal maturation: Pyrolysis study of the Mesoproterozoic Xiamaling marine shale from Zhangjiakou, Hebei, China[END_REF][START_REF] Yang | Evolution of nanopore structure in lacustrine organic-rich shales during thermal maturation from hydrous pyrolysis, Minhe Basin, Northwest China[END_REF]. Many of these papers compare their results with each other although they often used different thermal maturation systems and sample rock fabrics. It is clear from these results that this could introduce biases into the estimations of hydrocarbon production, permeability, gas storage capacity based on these results. For example, the amount of NSO (including asphaltenes) generated during cores maturation is 35-50% lower during the oil window than for powder and fragments (Fig. 1). Recent numerical models showed that the increase in the amount of generated asphaltenes may enhance the permeability of organic pores but reduce the permeability of inorganic pores and micro-fractures of shales (Cui, 2019a). The organic or inorganic pore permeability parameters of the same shale sample estimated by these mathematical models could, therefore, vary greatly for the same thermal maturity depending on the sample rock fabric used. Numerical models showed also that a change in pore sizes, distribution, connectivity, or shape will influence the permeability of the rock and its flow properties (Cui, 2019a(Cui, , 2019b)). Our results show that maturations on powder or fragments lead to different PSD and especially a lower number of micropores than maturations on cores. Recent computations showed that the PSD, and in particular the number of micropores in shales, has a strong influence on the gas in place calculation [START_REF] Tolbert | Quantifying Pore Size Distribution Effect on Gas in Place and Recovery Using SLD-PR EOS for Multiple-Components Shale Gas Reservoir[END_REF]. In our thermal maturation conditions, maturations of powder and fragments may, therefore, lead to an J o u r n a l P r e -p r o o f underestimation of the amount of gas in place calculated by these models compared to maturations on cores. In general, models for estimating the gas sorption/storage capacity of kerogen or gas in place in shales use specific surface area or pore volume data based on Langmuir models [START_REF] Alafnan | Petrophysics of Kerogens Based on Realistic Structures[END_REF][START_REF] Alafnan | Effect of Kerogen Thermal Maturity on Methane Adsorption Capacity: A Molecular Modeling Approach[END_REF][START_REF] Ambrose | Shale gas-inplace calculations part I: new pore-scale considerations[END_REF][START_REF] He | Hydrocarbon generation-retention-expulsion mechanism and shale oil producibility of the permian lucaogou shale in the Junggar Basin as simulated by semi-open pyrolysis experiments[END_REF][START_REF] Memon | An experimental study of gas sorption, adsorbed, and sorbed porosity, and their impact on shale gas-in-place calculations[END_REF][START_REF] Tolbert | Quantifying Pore Size Distribution Effect on Gas in Place and Recovery Using SLD-PR EOS for Multiple-Components Shale Gas Reservoir[END_REF][START_REF] Ungerer | Molecular Modeling of the Volumetric and Thermodynamic Properties of Kerogen: Influence of Organic Type and Maturity[END_REF]. The differences in pore volume and sizes observed between our thermal maturation experiments on cores, fragments, and powder, therefore, show that the choice of maturation parameters is crucial to ensure consistent estimates of shale reservoirs' properties. Most recent studies aimed to improve their models for predicting the quality and recoverability of the studied reservoirs using thermal maturation. It is therefore urgent to consider the effect of the experimental conditions used for thermal maturations to move towards standardization of thermal maturation techniques for a better and reliable understanding of the factors affecting hydrocarbon production, yields, and shale porosity (and without introducing biases due to differences in experimental maturation conditions between studies).

Conclusions

Laboratory thermal maturations of cores, fragments, and powder from the same organic-rich Kimmeridge Clay mudstone were compared. The results reveal that only a few differences of OM geochemical composition and porosity exist between rock fragments and powder series for each stage of maturation while cores give different results. Probably due to the lower surface contact between the organic components themselves and clay mineral surfaces (which can act as a catalyst for oil and gas generation) in cores, the OM undergoes a less effective thermal degradation characterized by (i) the production of lower SAT, ARO and NSO concentrations during oil production; (ii) lower C1-C5 and CO2 concentrations during gas generation. This is marked by lower/less interconnected pore volume and smaller pores in the cores. This work shows that the amount of oil and gas and porosity may be influenced by the sample particle size used for laboratory thermal maturation. These experiments highlight, therefore, the importance of considering the particle size of the rock that is being pyrolyzed when studying OM thermal degradation and its relation with mudstones porosity. However, if these maturations in a confined and anhydrous environment on powder and fragments overestimate the J o u r n a l P r e -p r o o f quantity of oil and C1-C5 hydrocarbons generated during thermal maturations compared to samples with an intact fabric (i.e cores), the opposite tendency has been observed in previous maturations in semiconfined environments. The comparison with equivalent (in terms of TOC and OM composition) naturally gas-mature rocks from the Vaca Muerta Fm. show that cores give the closest results to natural trends and appear thus, more suitable for artificial thermal maturation. Indeed, the use of powder or fragments leads to significant variations in composition and amount of generated hydrocarbons, specific surface areas, PSD, and pore volumes which can lead to a significant deviation from natural trends.

Tables

Tab.1. Location and main properties of the studied sample. J o u r n a l P r e -p r o o f (Cavelan et al., 2020b(Cavelan et al., , 2019b)). The PSD curves presented in the figure are considered to be the most representative among the three triplicates but PSD curves obtained for each triplicate are comparable (less than 10% of variation).

J o u r n a l P r e -p r o o f

  demonstrated that semi-closed pyrolysis of J o u r n a l P r e -p r o o f type-II lacustrine mudstones gives different results in terms of hydrocarbon retention-expulsion mechanisms and pore development when performed on a powdered sample or cores.

FiguresFig. 1 .

 1 Figures

  (Cavelan et al., 2019b); b(Cavelan et al., 2019a)

			Location
	Basin	Formation	Well		Depth (m)
	Cleaveland Basin (UK)	Kimmeridge Clay	Ebberston87		69.98
			Mineralogy (wt.%) a
	Quartz	Calcite	Gypsum	Clay	Pyrite	Albite
	8.7	17.6	0.8	53.6	5.7	3.0
			Rock Eval parameters b
	TOC (wt.%)	S1 (mgHC/g rock)	S2 (mgHC/g rock)	IH (mgHC/g TOC)	IO (mg CO2/g TOC)
	10.3	0.2	57.4	557		14
			Vitrinite reflectance (%) b
			0.43

a

J o u r n a l P r e -p r o o f

Acknowledgments

The authors would like to thank Mrs. Rachel Boscardin, Dr. Rémi Champallier, and Mrs. Nathalie Lottier for their support during experiments. Many thanks for the professional help of anonymous reviewers and editors which have greatly improved the quality of this work. This work was partly funded by the French National Research Agency (ANR) as part of the program "Investments d'Avenir" LabEx VOLTAIRE, 10-LABX-0100. This work was supported by the French council of the region Centre-Val de Loire.

1 MDTR: ratio of methyldibenzothiophene [START_REF] Radke | Maturity parameters based on aromatic hydrocarbons: Influence of the organic matter type[END_REF]; RMDTR (%) calculated with the equation of [START_REF] Radke | Application of aromatic compounds as maturity indicators in source rocks and crude oils[END_REF]. 2 DMPR: ratio of dimethylphenanthrene 597 [START_REF] Radke | Maturity parameters based on aromatic hydrocarbons: Influence of the organic matter type[END_REF] . RDMPR was calculated with the equation of [START_REF] Radke | Application of aromatic compounds as maturity indicators in source rocks and crude oils[END_REF]. 3 MPI-1: index of methylphenanthrene [START_REF] Radke | Maturity parameters based on aromatic hydrocarbons: Influence of the organic matter type[END_REF]. RMPI-1 calculated with 598 the equation of [START_REF] Boreham | Alternative calibration of the Methylphenanthrene Index against vitrinite reflectance: Application to maturity measurements on oils and sediments[END_REF] for Rv between 0.3-1.7% and Rv > 1.7%. 599

Declaration of interests

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

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:


Angesom Ataklity Tesfay

Eric Pierre Simon

Sofiane Kharbech

Laurent Clavier

Deep Learning-based Signal Detection for Uplink in LoRa-like Networks

Keywords: LoRa, IoT, deep learning, neural networks, capture effect

published or not. The documents may come   L'archive ouverte pluridisciplinaire

I. INTRODUCTION

In the next few years, the number of connected Internet of Things (IoT) devices is expected to grow exponentially, which requires technology that can handle large-scale connectivity. In recent years, Low-Power Wide Area Network (LPWAN) technologies, such as LoRa and SigFox in the license-free frequency band, have been introduced to deal with this issue. LoRa uses a Chirp-Spread Spectrum Modulation scheme. Different spreading factors (SF) and bandwidth settings can be used to achieve quasi-orthogonal transmissions. However, when two or more devices simultaneously transmit with the same SF on the same frequency band, collisions occur at the receiver [START_REF] Qadir | Analysis of the reliability of LoRa[END_REF], generally resulting in packet loss. In fact, sometimes, one packet can be correctly decoded despite the collision. This is the capture effect. This can be achieved when the desired signal is sufficiently stronger than the interfering one. Such results have been presented in LoRa communications in [START_REF] Edward | On the coexistence of LoRa-and Interleaved Chirp Spreading LoRa-Based modulations[END_REF]. However, an interleaved chirp spreading (ICS) modulation scheme is used, and this is not directly backward compatible with the LoRaWAN standard. This principle is also used in serial interference cancellation schemes. In [START_REF] Tesfay | Serial interference cancellation for improving uplink in LoRa-like networks[END_REF], the strongest user can be decoded, then suppressed, and the second strongest can, in turn, be decoded. The capture effect is also used in [START_REF] Rachkidy | Decoding superposed LoRa signals[END_REF], where two superposed signals, synchronized or slightly desynchronized, can be decoded using timing information. However, ensuring synchronization is not realistic in the uplink of wide-area networks considered in this paper.

Nevertheless, the capture effect requires a sufficient power gap between the desired and the interfering signal. In this paper, we propose two signal detection schemes for the uplink in LoRa-like networks based on deep learning that significantly improve the receiver's ability to accurately retrieve a signal, even when interfering users are present.

In the literature, some works employ the deep learning approach for IoT networks [START_REF] Zhang | High-Accuracy Entity State Prediction Method Based on Deep Belief Network Toward IoT Search[END_REF], [START_REF] Zhou | AAIoT: Accelerating Artificial Intelligence in IoT Systems[END_REF], but not for detection. To our knowledge, there are no works yet that investigate detection in that context using deep learning.

The contribution of this paper is three-fold: (i) Designing two new receivers based on deep learning for detection in LoRa networks. The first one deals with bit detection with a regression approach, as in [START_REF] Ye | Power of deep learning for channel estimation and signal detection in ofdm systems[END_REF] for the OFDM context, and the second one deals with symbol detection with a classification approach. These receivers can cope with interference coming from other LoRa users, and both strongly improve the capture effect compared to the classical LoRa receiver; (ii) It is shown that, once they have information on the power levels, the designed receivers are further efficient, mainly if the interfering users are stronger than the user to be decoded; and (iii) The outstanding performance of the new receivers is achieved while maintaining an efficient complexity order.

The rest of the paper is organized as follows. Section II describes the system model. Section III presents the proposed deep learning-based receivers. Section IV shows simulation results and discussions. Conclusions are drawn in Section V.

II. SYSTEM MODEL

In LoRa, each symbol carries SF bits, representing M = 2 SF possible values. The signal bandwidth is denoted by B, and the symbol duration is T s = M T , where T = 1/B. The SF ranges from 7 to 12. Each LoRa symbol, also called chirp, consists of a linear frequency shift over T s . It is built from a raw chirp c(t). The raw chirp has the instantaneous frequency B Ts t, yielding the base-band expression [START_REF] Reynders | Chirp spread spectrum as a modulation technique for long range communication[END_REF]:

c(t) = exp 2ıπ B 2T s t 2 t ∈ - T s 2 , T s 2 . ( 1
)
Let Q be the number of transmitted LoRa symbols in a packet. The transmitted symbol of the jth user at time qT s (q = 0,

• • • , Q -1) is m (q) j ∈ {0, ..., M -1}.
This symbol is carried by performing a cyclic shift of the raw chirp by δ (q) j = m (q) j T , as illustrated in Fig. 1 is:

s (q) j (t) =              exp 2ıπ B 2Ts t 2 + m (q) j Ts t , t ∈ -Ts 2 , Ts 2 -δ (q) j , exp 2ıπ B 2Ts t 2 + m (q) j Ts -B t , t ∈ Ts 2 -δ (q) j , Ts 2 .
(2)

The transmitted signal of user j, s j (t), is then

s j (t) = Q-1 q=0 s (q) j (t -qT s ).
The transmitted LoRa packet is obtained by adding a preamble composed of raw chirps. A circle of radius r max with one gateway located at the center is considered, including a user free guard-zone around the gateway with radius r min . We assume N i interfering users having the same SF. N i is randomly drawn from a Poisson distribution with parameter λ. The 2D coordinates of the N i interfering users are uniformly distributed while considering only positions within the disc defined by r max and r min . Since different devices operate autonomously, the transmission between nodes is asynchronous.

The synchronization is carried out as follows: a correlation is first performed between the received signal and the preamble. Then the highest peak of the correlation is selected, and the received signal is synchronized on it. The user corresponding to this peak is the desired user to be decoded, indexed by j here. The received signal associated with symbol q of user j sampled at

t = nT , n = -M 2 , • • • , M 2 -1 is: r (q) [n] = h j s (q) j [n] + i∈I h i s interf,(q) j,i [n] + w (q] [n], (3)
where s

(q) j [n] = s (q) j (nT ), I is the set of interfering users (|I| = N i ) and w q [n] ∼ CN (0, σ 2
) is a circularly symmetrical complex Gaussian noise. h j and h i are the channel coefficients for users j and i, respectively.

The collision between the qth symbol of the selected user j and two consecutive symbols of an interfering user i is illustrated in Fig. 2. The interfering part of user i is denoted by s interf,(q) j,i . The delay between user j and user i (accounting for asynchronous transmissions between the nodes) is τ j,i .

To demodulate, the classical LoRa receiver first multiplies the samples of the received signal by the conjugate of the raw chirp, yielding y (q) [n] = r (q) [n]c * [n], where c * [n] = c * (nT ). Then, a Fast Fourier Transform (FFT) is applied:

Y (q) [k] = M/2 n=-M/2 y (q) [n] e -2ıπ nk M , k = 0, . . . , M -1. (4)

Frequency Time

Fig. 2: Collision between a symbol of desired user and two consecutive symbols of the interfering user.

In the classical LoRa receiver, the symbol m (q) j is estimated by searching for the frequency index where the modulus of ( 4) is maximum. However, collisions occur when multiple users transmit simultaneously using the same SF, yielding possible detection errors.

III. PROPOSED DEEP LEARNING-BASED RECEIVER

This section proposes two receivers: the first is based on Deep Feedforward Neural Network (DFNN), and the second on Convolutional Neural Network (CNN).

A. Deep Feedforward Neural Network-based receiver

As indicated in Fig. 3, the detector relies on a DFNN architecture with four hidden layers. The number of nodes in each hidden layer is 8M , 4M , 2M , and M . The input is the modulus of the de-chirped received samples after the FFT (4), yielding M input nodes. The output is the bits of the transmitted symbol, yielding SF output nodes. The ReLU function is used as the activation function in the hidden layers. The sigmoid function is applied to map the outputs to the interval [0, 1] in the output layer. Batch normalization (BN) is embedded in the hidden layers to prevent overfitting. The DFNN is trained so that the error between the outputs and the transmitted bits is minimized. The error is evaluated through the L 2 mean squared error function. Then, the detected bits are obtained by thresholding the outputs. For the sigmoid function, the corresponding threshold is 0.5.

B. Convolutional Neural Network-based receiver

Differently from the common feedforward architecture, CNN relies mainly on convolution operations within the socalled convolutional layers. For this architecture, the input is presented as an M × M binary image containing the plots of (4), as in 5. The M at the output correspond to the M symbols to be detected. Here, we use a structure that includes two convolutional layers and two fully connected layers (cf. Fig. 4). We set M/4 and M/2 kernels for the first and second convolutional layers, respectively. The kernel size is set to 4 × 4 for both layers. A pooling layer follows convolution steps to reduce the feature map's dimension while keeping the most relevant information. The filter used for the average pooling layer is of size 2 × 2, and the stride is 2. The output of the second pooling layer is flattened to be the input of the fully connected layer. The first fully connected layer has 4M nodes and the second one has 2M nodes. Similarly to the DFNN, the ReLU function is used as the activation function, and batch normalization is performed. For the output classification layer, we employ the softmax function. The CNN is trained to minimize the crossentropy loss between the output and the transmitted symbols.

IV. SIMULATION RESULTS

In this section, we provide simulation results to evaluate the proposed DFNN and CNN-based receivers' performance. All the parameters of the neural network are set through a hyperparameter tuning process. Training is done singly offline using training data generated from simulation for each SNR value. For the LoRa modulation, the bandwidth B = 250 kHz is considered, with SF = 7, yielding M = 128. r max and r min are set to 1 km and 200 m, respectively. In LoRa, the devices are usually located far from the gateway since LoRa is designed for long range communications. The proposed detectors are compared with the classical LoRa detector in terms of symbol error rate (SER).

In the following, first, we present results where N i is a random variable. Then, in order to investigate the capture effect, the number of interfering users is forced to one (N i = 1). Capture effect is the receiver's ability to decode one out of two (or more) colliding users correctly, usually the strongest one but not necessarily with the proposed receiver.

A. Results for N i ∼ P(λ)

As described in section II, the total number of interfering users N i , in the time interval of interest, is drawn from a Poisson distribution, i.e., N i ∼ P(λ), with the following values for λ = 0.25, 0.5, 0.7, and 1. Training and test are done for each (SNR, λ) couple. Note that LoRa receivers can estimate the SNR. Results are presented in Fig. 6. As a lower bound, the case when there is no interfering user, i.e., λ = 0, is considered (dashed line curves). It is shown that the proposed receivers outperform the classical LoRa one for λ = 0. For λ = 0, the SER of the CNN is close to that of the classical LoRa receiver, the optimal in that case, whereas the DFNN is not as efficient with a 2 dB penalty for a target SER of 10 -4 . For the lowest number of interfering users (λ = 0.25 and 0.5), the SER of the two proposed receivers is in the order of 10 -3 for SNR = -7.5 dB (cf. Figs. 6a and6b).

For the same SNR value, in the case of the higher number of interfering users (λ = 0.7), the SER of the CNN-based receiver remains in the order of 10 -3 whereas the SER of the other receivers degrades (cf. Fig. 6c and6d). Besides, as the number of interfering users increases, the classical LoRa receiver endures higher performance loss than the CNN-based receiver.

B. Capture Effect -N i = 1
The goal is now to analyze the impact of one interfering user with different levels of signal-to-interference ratio (SIR). This is done by generating a new test set for which N i is forced to one and testing the previously trained networks with this specific test set. The one that was trained for the (λ = 0.25, SNR = -6 dB) couple is considered. Fig. 7 plots the obtained SER as a function of the SIR (solid line).

Additionally, as a perspective, we also investigate what would happen if the SIR could be estimated at the receiver. To do so, we do a new training of the networks for the different (SIR, SNR = -6 dB) couples with N i = 1, and we use the appropriate network for the test. Clearly, in this context, the deep learning-based approaches outperform the classical receiver for SIR ≤ 4 dB.

When SIR > 0 dB, that is, the selected user's signal is the strongest, the capture effect happens for all receivers while being improved for the CNN-based receiver when the SIR is under 6 dB. However, when SIR < 0 dB, i.e., the interfering user is the strongest, performance can be further improved if we have information on the SIR, i.e., learning is performed given the SIR value. In fact, when the SIR is known, the deep learning-based approach makes it possible to distinguish between the two users based on their power level, unless there is ambiguity, i.e., SIR = 0 dB.

C. Computational Complexity

The computational complexity of the classical LoRa receiver is of O(M log(M )) [START_REF] Tesfay | Multiuser detection for downlink communication in LoRa-Like networks[END_REF]. For the DFNN-based receiver, the complexity results in O(M 2 ). Based on [START_REF] He | Convolutional Neural Networks at constrained time cost[END_REF], where the complexity order for the convolutional layers is derived, the overall complexity of the CNN-based receiver is also of O(M 2 ). Even though the proposed receivers and the classical LoRa receiver have different complexity orders, the proposed schemes' running time remains polynomial. According to [START_REF] Kleinberg | Algorithm Design[END_REF], one of the definitions for computational efficiency is the following: "an algorithm is efficient if it has a polynomial running time". Besides, considering that this work focuses on the uplink case, where the gateway is plugged into a power source, issues of battery life and autonomy that may result from high complexity are not of concern.

V. CONCLUSION

This article investigated a deep learning-based approach for LoRa-like networks to decode a selected user's signals while considering interfering users that transmit simultaneously over the same frequency channel with the same SF. From the simulation results, we observed that in terms of SER, the two proposed receivers, the DFNN-based and the CNN-based, outperform the classical LoRa decoder in the presence of interference. In the absence of interference, the CNN-based receiver performs closely to the classical LoRa receiver, which is the optimal receiver for the interferencefree case. However, the DFNN-based receiver has endured a slight loss in performance, e.g., a 2 dB penalty in SNR for a target SER of 10 -4 . The good performance shown by the proposed schemes is achieved while keeping an efficient complexity order, i.e., a polynomial running time of order two.

For the future, the deep learning-based approach seems to be a promising candidate to tackle the issue of interference in LoRa networks due to the exponential growth of connected devices. In addition to improving the capture effect, i.e., decoding the useful user when its power is higher than that of the interference, we have shown that the deep-learning-based approach makes it possible to decode the useful user when it is weaker than the interference. This requires information on the SIR. As a perspective, an SIR estimation technique could be combined with the deep learning-based decoder to improve the receiver's performance further.

Fig. 1 :

 1 Fig. 1: (a) Raw chirp (b) Coded chirp associated with m (q) j .

Fig. 3 :

 3 Fig. 3: Deep Feedforward Neural Network architecture.

6 Fig. 5 :

 65 Fig. 5: Plot of |Y(q) [k]| (k = 0, • • • , M -1)for a symbol of m (q) = 20 with SNR = -10 dB and SF = 7.

= 1 Fig. 6 :

 16 Fig. 6: Symbol error rate as a function of the SNR for different detection approaches: classical decoder [ ], DFNN-based [ * ], and CNN-based [•]. For all figures, two scenarios are considered: no interference (plots in dashed line [ ]) and when interference can happen (plots in solid line [ ]). The plot inside show the probability mass function (pmf) related to the number of interfering users.

Fig. 7 :

 7 Fig. 7: SER as a function of the SIR for the classical detector [ ], the DFNN-based [ * ], and the CNN-based [•]. The SNR for the selected user is set to -6 dB. Two different cases are considered: plots in solid line [ ] networks trained for λ = 0.25, plots in dashed line [ ] networks trained for each SIR value.

  . Hence, the coded chirp Copyright (c) 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information. This article has been accepted for publication in IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, but has not been fully edited. Content may change prior to final publication. Citation information: A. A. Tesfay, E. P. Simon, S. Kharbech, and L. Clavier, "Deep Learning-Based signal detection for uplink in LoRa-Like networks," in 2021 IEEE 32nd PIMRC, Helsinki, Finland, Sep. 2021.

  This article has been accepted for publication in IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, but has not been fully edited. Content may change prior to final publication. Citation information: A. A. Tesfay, E. P. Simon, S. Kharbech, and L. Clavier, "Deep Learning-Based signal detection for uplink in LoRa-Like networks," in 2021 IEEE 32nd PIMRC, Helsinki, Finland, Sep. 2021.

		Convolutional	Convolutional	FC Layers
		Layer 1		Layer 2
	Input	Conv + ReLU + BN	Avg pool	Conv + ReLU + BN	Avg pool		Output
							softmax
						FC2 + BN
	Input					FC1 + BN	Output
	Layer		Hidden Layers	FC: Fully Connected BN: Batch Normalization	Layer

Fig. 4: Convolutional Neural Network architecture.

  This article has been accepted for publication in IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, but has not been fully edited. Content may change prior to final publication. Citation information: A. A. Tesfay, E. P. Simon, S. Kharbech, and L. Clavier, "Deep Learning-Based signal detection for uplink in LoRa-Like networks," in 2021 IEEE 32nd PIMRC, Helsinki, Finland, Sep. 2021.

	-16	-14	-12	-10	-8	-6	-4
				SNR (dB)

  This article has been accepted for publication in IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, but has not been fully edited. Content may change prior to final publication. Citation information: A. A. Tesfay, E. P. Simon, S. Kharbech, and L. Clavier, "Deep Learning-Based signal detection for uplink in LoRa-Like networks," in 2021 IEEE 32nd PIMRC, Helsinki, Finland, Sep. 2021.

	-10	-8	-6	-4	-2	0	2	4	6	8	10
						SIR (dB)

Copyright (c) 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.


Jérôme Labille

Danielle L Slomberg

Riccardo Catalano

Olivier Radakovitch

Samuel Robert

Marie-Laure Trémélo

Jean Luc Boudenne

Manasfi Tarik

Marie-Laure Apers-Trémélo

Jean-Luc Boudenne

Tarik Manasfi

Are sunscreen UV filters polluting our beaches? A case study from consumer habits to water analysis on the French Mediterranan Coast Coauthors

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Methodological approach

We assessed beach attendance through user counts and photographic images. Simultaneously, a questionnaire was carried out on-site between 8:00 am and 8:00 pm. We surveyed 471 people.

Beachgoers were asked speci c questions about their sunscreen use (type, frequency, and time of application) and their opinion regarding the possible e ects of these products on the marine environment. The containers of the suncare products used by the interviewees were photographed and the information on the packaging, such as brand, SPF, and composition were recorded. A total of 124 suncare product compositions were reported. This enabled us to estimate the average composition and maximum UV lter (i.e., organic and mineral) quantity that could be released daily into the beach water. During the peak recreational time, mineral UV lters were detected in: -higher concentrations in the bathing area than o shore, -higher concentration in top surface layer than in the water column:

UV filter quantification in bathing water

Concentrations of Ti and Zn found in di erent water fractions during peak attendance

in tsl and col respectively: TiO2: 100-900 > 20-50 µg/L ZnO: 10-15 > 1-3 µg/L

Methodological approach

On Saturday, July 15th, 2017, water samples were collected at 4:00 pm, during peak recreational activity. The spatial distribution of any UV lters in the waters was assessed by sampling at three distances from the shoreline in Bathing Zone 1 (BZ1), Bathing Zone 2 (BZ2) and beyond the Bathing zone (BBZ).

Water samples were collected at two depths:

-the top surface layer (tsl) (~ 1 cm), where hydrophobic compounds would likely be concentrated; -40 cm was selected as a constant sampling depth to represent the average water column (col).

Additional samplings were conducted at 8:00 am on Saturday, July 15 and Sunday, July 16, 2017, i.e. just before and after the studied peak attendance.

Mineral UV lter quanti cation: After digestion, quadrupole ICP-MS was used to analyse Ti and Zn concentrations as indicators of mineral UV lters. local geochemical background was acounted from the concentrations recovered beyond the bathing zone (BBZ).

Organic UV lters quanti cation: dioxybenzone (DIOXY), oxybenzone (OXY), avobenzone (AVO), 2-ethylhexyl-4-methoxycinnamate (OMC), and octocrylene (OC) were analysed using liquid chromatographic/mass spectrometry.

During the peak recreational time, organic UV lters were detected in: -higher concentrations in the bathing area than o shore, -higher concentration in top surface layer than in the water column:

OC: 40-420 > 30-150 ng/L AVO: 10-15 > 10-350 ng/L

The three UV lters are found in the water column only in the bathing zone and during recreational activity.

This trend was not observed in the top surface layer (tsl), where residence time must be shorter.

In order to assess the release of UV lters from the sunscreen used by beachgoers into seawater within the bathing zone, a eld campaign was carried out during the summer of 2017 at three beaches in Marseille, along the French Mediterranean coast. A social survey analyzed beachgoer attendance, the quantities and types of suncare products used and the bathing frequencies, while the bathing water was analyzed spatially and temporally so as to quantify both mineral and organic UV lters directly released and recovered.

During the peak recreational time at the three beaches, both mineral and organic UV lters were detected in higher concentrations in the bathing area than o shore. In general, higher concentrations were recovered in the water top surface layer than in the water column, giving respectively 100-900 and 20-50 µg/L for TiO2, 10-15 and 1-3 µg/L for ZnO, 40-420 and 30-150 ng/L for octocrylene, and 10-15 and 10-350 ng/L for avobenzone. More than 75% of the 471 interviewees reported bathing every time they go to the beach, with 68% using a suncare product 2.6 times on average. From these data we estimated that an average mass of 52 kg/day or 1.4 t/month of suncare products are possibly released into bathing water for a beach attended by 3,000 people daily. The mass ratio of UV lters in such products typically ranges from 0.03 to 0.1, allowing us to propose theoretical maximum concentrations in the beach water. Our recovery of measured UV lter concentrations in seawater compared to the theoretical concentrations revealed two distinct scenarios for the mineral and organic lters. While up to 45% of the mineral lters used by beachgoers may be released into the seawater, the organic lters were minimally recovered in the environment, most likely due to internalization through the skin barrier or partial photodegradation.

Abstract

Aims of this work

-to estimate the daily ux of sunscreen and UV lters transferred from beachgoers into the bathing water on a standard summer day; -to study the possible co-occurrence of organic and mineral UV lters in seawater, both in the water top surface layer and water column; -to determine, for the rst time, the patterns of UV lter occurrence in the bathing water at three French Mediterranean beaches. This study is the rst eld campaign coupling chemical water analysis to quantify both organic and mineral UV lters in seawater, with a simultaneous social survey of consumer's habits on the beach.

Scenarios of UV lter release to the water usage cycle

Among the variety of scenarios of environmental release, this work deals the direct release via recreational activities.

  organic UV filters of nano size organic UV filtersUV lter occurrence among the suncare products used on the three beachesData recovered from 124 photographies of products. Speci c UV absorption range of each lter is given as / lter name -UV range/. For mineral UV lters, the occurrence of non-nano and nano forms are given; an overlap exists for TiO2 when both forms are labeled on products

  the suncare products used on the beach Solar protection factors of the suncare products used on the beach What are your three primary criteria for selecting a suncare product? Do you think that suncare products can impact the quality of marine bath water? you practice bathing? If yes, how many times do you bathe per visit? Do you practice whole body immersion?

Field

  localisation, Marseille, Mediterranean coast, France Sampling points on the three beaches studied Time evolution of organic UV lter concentrations at the three distances from shore line at Prophète Beach Concentrations in water column (col) are compared to those in the top surface layer (tsl).

UV filter i %UVi mUVi /