Fabrice Extramiana
email: fabrice.extramiana@aphp.fr

Pierre-Léo Laporte

Martino Vaglio

Isabelle Denjoy

Pierre Maison-Blanche

Fabio Badilini

Antoine Leenhardt

Prof Fabrice

Computerized automated algorithm-based analyses of digitized paper ECGs in Brugada syndrome

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

Introduction

Brugada syndrome (BS) is a rare inherited arrhythmic syndrome with a coved type 1 ST-segment elevation on the ECG precordial leads and an increased risk of sudden cardiac death (SCD) [START_REF] Wilde | Study Group on the Molecular Basis of Arrhythmias of the European Society of Cardiology. Proposed diagnostic criteria for the Brugada syndrome[END_REF][START_REF] Priori | ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC)[END_REF].

While the majority of affected Brugada patients are asymptomatic, the syndrome is the cause of a significant proportion of SCDs without structural heart disease, usually in young and otherwise healthy subjects [START_REF] Priori | ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC)[END_REF].

Previous SCD, syncope, familial history, the presence of a spontaneous type 1 pattern, a positive EP study and sinus node dysfunction have been described as risk factors for severe arrhythmic events during follow-up [START_REF] Priori | ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC)[END_REF][START_REF] Antzelevitch | J-Wave syndromes expert consensus conference report: Emerging concepts and gaps in knowledge[END_REF]. In survivors of cardiac arrest and in Brugada patients with cardiac syncope, prevention of SCD is based on implantable cardioverter defibrillators (ICDs) [START_REF] Priori | ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC)[END_REF]. However, the decision to implant an ICD in asymptomatic patients can be problematic, as indicated by the low level of evidence [START_REF] Priori | ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC)[END_REF][START_REF] Antzelevitch | J-Wave syndromes expert consensus conference report: Emerging concepts and gaps in knowledge[END_REF][START_REF] Sieira | A score model to predict risk of events in patients with Brugada Syndrome[END_REF] and disappointing predictive scores in low-and intermediate-risk Brugada patients [START_REF] Probst | Robustness and relevance of predictive score in sudden cardiac death for patients with Brugada syndrome[END_REF].

Although the mechanism of arrhythmias in BS is still debated, it is accepted that ECG abnormalities reflect the underlying arrhythmic substrate [START_REF] Behr | Brugada syndrome and reduced right ventricular outflow tract conduction reserve: a final common pathway?[END_REF], thus supporting the hypothesis that ECG parameters could improve risk stratification in BS. Accordingly, many studies have evaluated the risk stratification performance of ECG-derived parameters [START_REF] Antzelevitch | J-Wave syndromes expert consensus conference report: Emerging concepts and gaps in knowledge[END_REF][START_REF] Gourraud | Brugada syndrome: Diagnosis, risk stratification and management[END_REF].

In early studies, Brugada patient cohorts mainly included paper ECGs, and most studies were consequently based on manual measurements evaluating one or a few ECG parameters [START_REF] Morita | Identification of electrocardiographic risk markers for the initial and recurrent episodes of ventricular fibrillation in patients with Brugada syndrome[END_REF][START_REF] Mugnai | Role of Electrocardiographic Tpeak-Tend for the Prediction of Ventricular Arrhythmic Events in the Brugada Syndrome[END_REF][START_REF] Maury | Prevalence and prognostic role of various conduction disturbances in patients with the Brugada syndrome[END_REF][START_REF] Delinière | Prediction of ventricular arrhythmias in patients with a spontaneous Brugada type 1 pattern: the key is in the electrocardiogram[END_REF][START_REF] Delinière | Prognostic significance of a low T/R ratio in Brugada syndrome[END_REF][START_REF] Giustetto | Ventricular conduction delay as marker of risk in Brugada Syndrome. Results from the analysis of clinical and electrocardiographic features of a large cohort of patients[END_REF]. These manual measurements are often difficult and time consuming and have poor accuracy and reproducibility. Conversely, automatic measurements from digital ECG can provide a more comprehensive and reproducible set of parameters. Unfortunately, Brugada syndrome is a rare disease with a low incidence of events, and gathering large digital ECG cohorts with adequate follow-up could take years, if not decades.

Converting paper ECGs into digital forms (digitization) has been proposed as a means to automatically analyze paper ECGs in different clinical settings [START_REF] Bhullar | A computer based system for the study of QT intervals[END_REF][START_REF] Badilini | ECGScan: a method for conversion of paper electrocardiographic printouts to digital electrocardiographic files[END_REF] and could be a viable solution.

In the present study, we hypothesized that paper ECGs acquired from Brugada patients are suitable for digital quantitative analysis.

We aimed: 1) to validate the digitization process for paper ECGs; and 2) to quantify the acute class I antiarrhythmic drug effects on relevant ECG parameters in Brugada syndrome.

Methods

ECG selection and processing

From our reference center BS cohort, we retrieved paper ECGs of patients presenting with at least one type 1 BS pattern ECG (either spontaneous or drug induced).

Paper ECG printouts (in 12x1, 6x2 or 4x3 display format presenting with a visible grid) were scanned (600 dpi resolution) and then digitized using ECGScan software, version 3.4.0 (AMPS, LLC, New York, NY, USA) to obtain FDA HL7 XML format ECGs [START_REF] Badilini | ECGScan: a method for conversion of paper electrocardiographic printouts to digital electrocardiographic files[END_REF]. The full 10-second 12-lead ECG was subsequently reconstructed by replicating the average beat of each lead computed from available digitized data.

A subset of 20 patients who also had digitally recorded ECG were used for a validation analysis. These digital ECGs were printed and subsequently digitized. These 20 digital and digitized ECGs were used for digitization process validation. ECGs from BS patients with both spontaneous nontype 1 BS ECGs (baseline) and drug-induced type 1 BS ECGs were used to quantify the acute class I antiarrhythmic drug effects on ECG parameters.

ECG parameters and automated measurements

Automated measurements were obtained combining the outputs from the Bravo and Glasgow algorithms, which are embedded in CalECG software, version 4.1.0 (AMPS, LLC, New York, NY, USA).

The protocols of both algorithms are based on the representative beats of each lead, which are computed by CalECG on the reconstructed 10-second 12-lead ECG. The analysis is thus performed on the same waveforms without data selection bias. The rationale for using a combined analysis was to cover the widest spectrum of Brugada-related sensitive parameters.

The complete list of ECG measurements obtained with the Bravo and Glasgow algorithms is provided in the supplemental data.

The following global parameters were considered: RR, PR, QRS, QT intervals, Bazett's and Fridericia's QTc, ST and T duration, and QRS frontal axis.

The lead-specific parameters for leads V1, V2 and V3 were: QRS duration, QRS area, total area of repolarization after the J point (A Tot J), its positive and negative components (A Tot + J and A Tot -J), ST amplitude at J point, ST-segment duration, ST slope (slope between the J point and 3/8 of the ST-T segment), T maximum positive amplitude (T+ Amp), T maximum negative amplitude (T-Amp), total area of repolarization of the T-wave (A Tot T), its positive and negative components (A Tot + T and A Tot -T), and Tpeak-Tend interval (TpTe) (Figure 1).

Statistical analysis

Categorical variables are expressed as percentages, and continuous variables as means ± SDs for normal distributions and medians with interquartile ranges (IQRs) for nonnormal distributions. The

Results

Study population

A type 1 BS pattern paper ECG was available in 353 patients. The type 1 pattern was spontaneous in 149 patients and drug induced in 204. Spontaneous type 1 ECGs were not used in the present study.

Of the 408 paper ECGs (1 baseline and 1 on-drug ECG for 204 patients), quality was not sufficient for digitization in 28 cases (7% of the 408 paper ECGs; 10/204 baseline ECGs and 18/204 drug-induced type 1 ECGs).

Consequently, 176 patients (30% females, mean age 43±13 years old, range 16 -75) had digitized ECGs both at baseline and with a drug-induced type 1 BS pattern. Ajmaline (1 mg/kg IV in 3 minutes) was used to induce a type 1 pattern in all but 5 patients, in whom flecainide (2 mg/kg IV in 10 min) was used.

Digitization process validation

ECG parameters obtained from digital and digitized ECGs were closely correlated (r=0.96±0.07, R²=0.93±0.12).

The Bland and Altman bias and 1.96*SD were 0.1±0.9 beats per minute for heart rate, -3.3±14.3 ms for global QRS and 0.4±10.3 ms for global QT duration.

Figure 2 shows the correlations between ECG parameters automatically measured from digital and digitized ECG on leads V1, V2 and V3.

The Bland and Altman bias±1.96*SD was -0.9±2.8 µV for ST amplitude, 0.6±8.3 for ST slope and 3.7±6.9 µV for T-wave amplitude.

Class I antiarrhythmic drug effects on relevant ECG parameters

Global parameters measured at baseline and upon drug challenge are shown Table 1. Class I antiarrhythmic drugs significantly shortened the RR interval and prolonged the PR, QRS and QT intervals.

Table 2 shows automated measurements of parameters of the QRS, the ST-T and the T-wave on the 3 right precordial leads. Class I antiarrhythmic drug significantly increased global QRS duration and area, ST-segment elevation (ST Amp), and the area of the ST-T segment after the J-point (A tot J), decreased the ST slope toward more negative values, and increased the area of the negative component of the T wave and the Tpeak-Tend interval (TpTe) (Table 2).

Figure 3 shows the individual changes in ST amplitude and ST slope on lead V2.

The mean increase in ST amplitude and decrease in ST slope were observed, but interindividual variability was present. The ECG tracings of the 2 outliers for ST amplitude on lead V2 showed that the algorithm could correctly identify the J point position and measure an accurate ST elevation amplitude (Figure 2).

Discussion

The goal of this work was to assess the feasibility of automated analysis of digitized ECGs from Brugada patients. We performed a 2-step validation.

First, we found that automated measurement of depolarization and repolarization ECG parameters from digital and subsequently digitized paper ECGs from Brugada patients can be reliably assessed. We then showed that the effects of a class I AA drug challenge on ECG parameters relevant for BS diagnosis could be automatically quantified.

Digitization

Digitization and subsequent analysis of paper ECGs is frequently used in the context of pharma thorough QT studies as a bridge before using only digitally recorded ECGs [START_REF] Bhullar | A computer based system for the study of QT intervals[END_REF][START_REF] Badilini | ECGScan: a method for conversion of paper electrocardiographic printouts to digital electrocardiographic files[END_REF][START_REF] Hingorani | Differences between QT and RR intervals in digital and digitized paper electrocardiograms: contribution of the printer, scanner, and digitization process[END_REF]. Accordingly, Paper printouts do not always display a full 10 seconds of all acquired leads. Since many automated algorithms use 10-sec recordings, this fact could be a problem for analyzing digitized ECGs. Therefore, we reconstructed a 10-second ECG by replicating available ECG data. This method could be difficult to apply in cases of poor-quality ECG and/or baseline drifting. It could also falsely minimize beat-to-beat variability and might be associated with the loss of the noise reduction obtained by the beat averaging process.

analyses
To validate the digitization process on ECGs recorded in Brugada patients, we first performed a benchmark validation on identical ECGs digitally recorded and subsequently printed, scanned, and digitized. The correlations and Bland and Altman plot results can be considered acceptable in relation to the values of the ECG parameters. In addition, ECG parameters relevant for Brugada ECG pattern description were also closely correlated (Figure 2). It is noteworthy that the validation was fully automated, without visual validation or caliper position manual changes.

Ajmaline effect

In addition to the above-described bench validation, we decided to further validate the digitization process by quantifying the effects of sodium channel blockade on ECG parameters in Brugada patients.

This validation was used as a surrogate to manually measure all ECG depolarization and repolarization parameters, which would have been almost impossible for the >100 ECG parameters automatically measured in 352 ECGs. Our aim was hence to validate the ability of fully automated measures from digitized ECGs to detect a pathophysiological effect.

We used 2 available ECG algorithms (Bravo AMPS LLC and Glasgow) that include measurements of Jpoint and ventricular repolarization-related parameters. However, digitized ECGs could also have been analyzed using other commercially available devices. It is also conceivable to analyze digitized ECGs with customized algorithms to better characterize the Brugada ECG pattern, for instance, without assumptions about the J-wave position [START_REF] Extramiana | Quantitative assessment of ST segment elevation in Brugada patients[END_REF].

We were able to quantify drug-induced PR, QRS and QT interval prolongation on the overlap median, as well as on the right precordial leads, and the effect of sodium channel blockade on relevant parameters in BS, such as ST amplitude at the J-point, ST slope and T-wave amplitude. Therefore, automated measurements from digitized ECGs appropriately recapitulated the expected effects of sodium channel blockade on the depolarization-repolarization process on ECG parameters from BS patients [START_REF] Shimizu | Effect of sodium channel blockers on ST segment, QRS duration, and corrected QT interval in patients with Brugada syndrome[END_REF].

Usefulness and perspective

Scanning an image of a paper ECG could be sufficient to avoid historical paper ECGs from fading in patients' files. Digitization could be further used to secure ECG tracing in a format allowing physicians and/or researchers to run automated computerized quantitative ECG analyses with high reproducibility and high output.

This method could be applied to the backload of paper ECG historical databases in Brugada syndrome and in other channelopathies or rare diseases to validate previous risk stratification studies [START_REF] Gourraud | Brugada syndrome: Diagnosis, risk stratification and management[END_REF][START_REF] Morita | Identification of electrocardiographic risk markers for the initial and recurrent episodes of ventricular fibrillation in patients with Brugada syndrome[END_REF][START_REF] Mugnai | Role of Electrocardiographic Tpeak-Tend for the Prediction of Ventricular Arrhythmic Events in the Brugada Syndrome[END_REF][START_REF] Maury | Prevalence and prognostic role of various conduction disturbances in patients with the Brugada syndrome[END_REF][START_REF] Delinière | Prediction of ventricular arrhythmias in patients with a spontaneous Brugada type 1 pattern: the key is in the electrocardiogram[END_REF][START_REF] Delinière | Prognostic significance of a low T/R ratio in Brugada syndrome[END_REF][START_REF] Giustetto | Ventricular conduction delay as marker of risk in Brugada Syndrome. Results from the analysis of clinical and electrocardiographic features of a large cohort of patients[END_REF] and to look for and test multiparameter models for risk stratification [START_REF] Tadros | Predicting cardiac electrical response to sodium-channel blockade and Brugada syndrome using polygenic risk scores[END_REF]. In addition, digitized ECGs would be easy to share for collaborative studies and could greatly increase opportunities for any type of computerized ECG signal analysis (time domain, frequency domain, vectocardiographic approaches, CineECG, artificial intelligence, etc.).

Limitations

Some limitations should be acknowledged. First, the limitations of digitization have been well identified [START_REF] Waits | Digitizing paper electrocardiograms: Status and challenges[END_REF], digitization is not possible for 100% ECG, and part of the process is not automated and is time consuming.

Second, we did not manually measure on the paper ECGs all of the parameters that have been automatically measured from digitized ECGs. Such a validation would, however, have been technically challenging for "complex" parameters, such as areas, slopes, and derived parameters. Even for simpler parameters, such as intervals, measurement of ECG parameters would have been very difficult to perform with acceptable precision and reproducibility for tenths of parameters on hundreds of ECGs.

However, in clinical settings for diagnostic purposes, it might be warranted to indicate measured relevant values on the ECG output to allow for visual qualitative validation.

Third, the selection of analyzed ECGs by an expert physician in inherited arrhythmic syndromes could have introduced a systematic bias. However, this bias was probably minimized by the study design, which included both type 1 and non-type 1 Brugada pattern ECGs, as well as by the intrinsically blind automated computerized analyses.

Finally, risk stratification based on parameters automatically measured from digitized ECGs will need to be validated in prospective BS patient cohorts.

Conclusion

In conclusion, automated algorithm-based measurements of depolarization and repolarization parameters from digitized paper ECGs are reliable and could quantify the acute effects of class 1 antiarrhythmic drug challenge in Brugada patients. Historical cohorts with only paper ECG recordings could be used in the search for prognostic ECG parameters in Brugada syndrome.

Acknowledgment

We are thankful to Professor Peter W. Macfarlane for his help with the Glasgow algorithm.

7. The upper right insert shows the ECG tracings of the 2 outliers for ST amplitude on lead V2.

  have focused mainly on standard ECG parameters (RR, PR, QRS and QT intervals), to the best of our knowledge, digitization has seldom been used in the context of Brugada syndrome. Digitization of ECGs from our Brugada syndrome clinical cohort with long follow-up was successfully performed for >90% of ECGs. The reasons for unsuccessful digitization were in most cases fading of the grid, preventing calibration. Drifting of the baseline and individual lead signals crossing each other could explain other failures to digitize paper ECGs.

Figure 1 .Figure 2 .

 12 Figure 1. Example of the "Brugada relevant" ECG parameters measured in the study

Figure 3 .

 3 Figure 3. Individual changes in ST amplitude and ST slope on lead V2.

Table 1 .

 1 Global ECG parameters measured at baseline and on class I antiarrhythmic drugs

	Parameter	Baseline ECG	ECG on class I	P value
			antiarrhythmic drug
	RR interval (ms)	872±172	763±144	<0.0001
	PR interval (ms)	154±33	183±44	<0.0001
	QRS interval (ms)	113±20	138±23	<0.0001
	QRS frontal axis	41 IQR(2; 65)	22 IQR (-27; 74)	0.09
	QT interval (ms)	393±36	405±43	<0.001
	QTcB (ms)	424±34	465±34	<0.0001
	QTcF (ms)	413±28	446±32	<0.0001
	ST duration (ms)	75±31	41±27	<0.0001
	T duration (ms)	206±35	226±44	<0.0001

Table 2 .

 2 Automated measurements of parameters on the 3 right precordial leads

	Parameter	Lead	Baseline ECG	ECG on class I	P value
				antiarrhythmic
				drug
	QRS duration	V1	93±21	112±30	<0.0001
		V2	97±21	118±26	<0.0001
		V3	96±17	119±25	<0.0001
	QRS Area	V1	-13044±11840	-4177±16941	<0.0001
		V2	-9107±18985	5541±26289	<0.0001
		V3	-1662±22065	70068±43882	<0.0001
	A Tot J	V1	18146±9380	34092±19486	<0.0001
		V2	47039±32049	59562±35310	<0.01
		V3	68751±43579	69010±46196	NS
	A Tot + J	V1	8307±9065	13791±11192	<0.0001
		V2	45307±33416	43904±37435	NS
		V3	68454±43846	64379±48150	NS
	A Tot -J	V1	-9834±10388	-20294±17120	<0.0001
		V2	-1729±6664	-15651±22269	<0.0001
		V3	-294±881	-4626±15095	<0.01
	ST amp	V1	42±46	142±119	<0.0001
		V2	110±84	338±227	<0.0001
		V3	98±77	174±180	<0.0001
	ST dur	V1	105±38	65±34	<0.0001
		V2	92±35	60±36	<0.0001
		V3	93±28	61±33	<0.0001
	ST Slope	V1	-8.7±12.2	-33.6±16.4	<0.0001
		V2	14.9±23.3	-27.4±28.5	<0.0001
		V3	33.7±19.1	23.1±29.9	<0.0001
	T+ Amp	V1	39±62	119±121	<0.0001
		V2	312±247	329±221	NS
		V3	487±287	416±300	<0.01
	T-Amp	V1	-109±90	-173±119	<0.0001
		V2	-20±65	-122±148	<0.0001
		V3	-8±80	-25±85	NS
	A Tot T	V1	12025±9206	18590±14342	<0.0001
		V2	36963±29441	25624±23920	<0.01
		V3	59484±38910	43415±37558	<0.0001
	A Tot + T	V1	2657±5845	2039±4518	NS
		V2	35340±30555	14029±24316	<0.0001
		V3	59291±39107	41009±39201	<0.0001
	A Tot -T	V1	-9362±9825	-16545±14608	<0.0001
		V2	-1622±6426	-11590±15829	<0.0001
		V3	-192±803	-2403±7310	<0.01
	TpTe	V1	81±20	104±29	<0.0001
		V2	88±18	104±33	<0.0001
		V3	93±18	104±28	<0.001

measurement agreement between digital and digitized ECGs was evaluated with correlations and Bland and Altman plots. Categorical variables were compared using Pearson's χ 2 test or Fisher's exact test, as appropriate. Continuous variables were compared using the paired samples t-test or the Kruskal Wallis test, as appropriate. A p value <0.05 was considered significant.


Vinicius De Almeida
email: vinicius.de-almeida@emse.fr

Eric Serris

Ana Cameirao

Jean-Michel Herri

Emilie Abadie

Philippe Glenat

Monitoring gas hydrates under multiphase flow in a high pressure flow loop by means of an acoustic emission technology

Keywords: natural gas hydrates, tracking hydrate particles, acoustic emission, oil-water dispersions, flow loop experiments

The formation of natural gas hydrates in oil and gas pipelines is an important concern due to the risk of hydrate blockage. In order to reduce the expenses with the use of chemicals to avoid hydrate plugging, more studies about the slurry flow and the mechanisms of blockage in flowing conditions are necessary.

In this study, it is presented an experimental work combined with a multi-instrumental data analysis to allow monitoring hydrate formation and track the particles in time and space. Acoustic emission (AE) is used in a high pressure flow loop apparatus to capture acoustic waves generated by the flow and to detect the presence of hydrates flowing in the system. The tests are conducted with Kerdane oil, saline water and natural gas at 75 bar and 4 °C. Two tests are presented, one at 30 and another at 80% water cut (water fraction in volume). It is shown that, with the absolute energy obtained from the AE sensors, it was possible to detect the beginning of hydrate crystallization and the hydrates displacement in the pipeline, because the absolute energy increases as the collisions of particles generate more acoustic emission. The results show that, for the tests performed, the flow became heterogeneous after hydrate formation, with some regions of the flowing volume containing more hydrates than others. It is also shown that the nonflowing volume of hydrates with entrapped liquid, caused by deposition or settling, can be estimated with AE.

Introduction

Ensuring the safe multiphase transport in offshore petroleum pipelines can be a challenge in flow assurance, mainly due to gas hydrate crystallization. The delivery of all the drained liquid extracted from the reservoir to the topside separator is often threatened by hydrate crystallization because the solid particles may plug the system [START_REF] Sloan | Clathrate Hydrates of Natural Gases -3rd edition[END_REF][START_REF] Sloan | Natural Gas Hydrates in Flow Assurance[END_REF][START_REF] Cardoso | Design Options for Avoiding Hydrates in Deep Offshore Production[END_REF]. There are two main strategies used by the industry to avoid hydrate blockage: hydrate avoidance and hydrate management. The first one consists in a complete hydrate avoidance by means of thermodynamics [START_REF] Nasir | A review on the role and impact of various additives as promoters/inhibitors for gas hydrate formation[END_REF][START_REF] Qasim | Quaternary ammonium salts as thermodynamic hydrate inhibitors in the presence and absence of monoethylene glycol for methane hydrates[END_REF][START_REF] Deka | Generation of methane gas hydrate equilibrium curve for the thermodynamic gas hydrate inhibitor propylene glycol[END_REF]. The second strategy consists in accepting hydrate formation as long as hydrates transport is stable and offers no risk to the production [START_REF] Sloan | A changing hydrate paradigm -from apprehension to avoidance to risk management[END_REF][START_REF] Kinnari | Hydrate Management in Practice[END_REF][START_REF] Olajire | Flow assurance issues in deep-water gas well testing and mitigation strategies with respect to gas hydrates deposition in flowlines-A review[END_REF]. Hydrate management is preferred in terms of costs, but there are uncertainties about its safety in many scenarios. The extension of the hydrate management strategy to more critical conditions requires a better understanding about the mechanisms of hydrate formation and plugging under flowing conditions, and this is still a challenge even in laboratory tests.

Flow loops are experimental pilots that can be used to study hydrate particles formation under flow and to understand the mechanisms of deposition, agglomeration and plugging [START_REF] Fidel-Dufour | Rheology of methane hydrate slurries during their crystallization in a water in dodecane emulsion under flowing[END_REF][START_REF] Joshi | Experimental flowloop investigations of gas hydrate formation in high water cut systems[END_REF][START_REF] Yan | Flow characteristics and rheological properties of natural gas hydrate slurry in the presence of anti-agglomerant in a flow loop apparatus[END_REF][START_REF] Melchuna | Topological modeling of methane hydrate crystallization from low to high water cut emulsion systems[END_REF][START_REF] Aman | Hydrate formation and deposition in a gas-dominant flowloop: Initial studies of the effect of velocity and subcooling[END_REF][START_REF] Ding | Investigation of natural gas hydrate slurry flow properties and flow patterns using a high pressure flow loop[END_REF][START_REF] Pham | Experimental study and modelling on methane hydrates crystallization under flow from a water-oil dispersion at high water cut[END_REF][START_REF] Liu | A high-pressure visual flow loop for hydrate blockage detection and observation[END_REF][START_REF] Liu | Hydrate slurry flow characteristics influenced by formation, agglomeration and deposition in a fully visual flow loop[END_REF][START_REF] Liu | Hydrate blockage observation and removal using depressurization in a fully visual flow loop[END_REF]. A common procedure in experiments with hydrates is to monitor temperature and gas consumption to know when the crystals are forming and to estimate the hydrate fraction and water conversion. The presence of hydrates often causes variations in pressure drop, density and flow rate as well. Despite many recent advancements in measurements and visualizations presented in the literature, the size of this type of apparatus brings challenges in terms of monitoring hydrate crystallization and tracking the particles in time and space.

Acoustic emission (AE) is a promising non-intrusive technology that can be used to track hydrates in flowing systems. AE was previously used to monitor pharmaceutic crystallization process [START_REF] Gherras | Monitoring industrial pharmaceutical crystallization processes using acoustic emission in pure and impure media[END_REF], to characterize the flow noise in gas-liquid flows [START_REF] Fang | Flow noise characterization of gas-liquid two-phase flow based on acoustic emission[END_REF], to monitor slug flow [START_REF] Al-Lababidi | Upstream Multiphase Flow Assurance Monitoring Using Acoustic Emission[END_REF], to identify the flow pattern in two-phase flow [START_REF] Husin | Feasibility study on the use of the Acoustic Emission technology for monitoring flow patterns in two phase flow[END_REF], for application in gas-solid flows [START_REF] Hii | An investigation of the generation of Acoustic Emission from the flow of particulate solids in pipelines[END_REF], to detect the presence of sand and droplets in pipelines [START_REF] El-Alej | Identification of minimum transport condition for sand in two-phase flow using acoustic emission technology[END_REF][START_REF] El-Alej | Monitoring the presence of water and water-sand droplets in a horizontal pipe with Acoustic Emission Technology[END_REF], to study the impact of abrasive particle in a slurry flow [START_REF] Droubi | Monitoring acoustic emission (AE) energy in slurry impingement using a new model for particle impact[END_REF][START_REF] Droubi | Monitoring acoustic emission (AE) energy of abrasive particle impacts in a slurry flow loop using a statistical distribution model[END_REF], among others. The first application of AE in flow loop tests with gas hydrates was presented by Cameirao et al. (2018), showing the potential of this technique to monitor the beginning of crystallization, agglomeration, and plug/sedimentation.

In this work, the absolute energy obtained from AE measurement is used to detect gas hydrates flowing in a pipeline and to obtain the spatial distribution of particles. The procedure here is to analyze the trend of the absolute energy before and after hydrate formation and to follow the evolution of the signals as hydrates form. Another important application of the technique is to estimate hydrate deposition or accumulation in singularities of the system.

2.

Experimental methodology for monitoring gas hydrates in a flow loop

Flow loop description

The Archimede flow loop, illustrated in Figure 1, is a pipeline of 56 meters for circulating liquid under high pressure and controlled temperature. It is composed by a separator, at the top of the flow loop,

where the natural gas is injected and there is contact between the circulating liquid and the gas. The injection of gas can be fast and manual or slow with an automatic injection with a gas flow meter. The pipeline is divided into three sections: a vertical downward section of approximately 9.5 meters, a slightly downward section (inclination of -3.6°) of approximately 36 meters and a vertical upward section of approximately 10.5 meters. The vertical sections have a pipe diameter of 15.7 mm, while the slightly inclined section has a pipe diameter of 10.2 mm. The power for circulating the liquid is provided by a Moineau pump with a flow capacity ranging between 100-500 L/h (which corresponds to 0.14-1.70 m/s).

The apparatus has a total of ten temperature probes (with a span of 0.1 °C) whose one measures the room temperature, two absolute pressure probes (one for the separator and another one for the gas injection system, with an span of 0.01 bar), four differential pressure probes (∆ , ∆ and ∆ with an uncertainty of 120 Pa±2.5⨯10 -4 p, ∆ with an uncertainty of 10 Pa ± 2.5⨯10 -4 p), an automatic gas injection with a gas flow meter (minimum span of 0.01 ln/minute), and a Coriolis that measures the density (with an uncertainty of ±2.0 kg/m 3 ) and the flow rate of the liquid that circulates in the loop (uncertainty of 0.2%).

Figure 1. Schematic illustration of the Archimede flow loop.

The pilot is also equipped with two in situ probes: a Focused Beam Reflectance Measurement (FBRM) installed in the downward section, and a Particle Vision and Measurement (PVM) in the slightly inclined section. These two instruments give information of the flow pattern and chord length distribution at microscopic scale [START_REF] Melchuna | Topological modeling of methane hydrate crystallization from low to high water cut emulsion systems[END_REF][START_REF] Pham | Experimental study and modelling on methane hydrates crystallization under flow from a water-oil dispersion at high water cut[END_REF]. From the FBRM, it is obtained the chord counts distribution in a range 1-1000 µm. From the PVM, it is obtained images of the flow in a range 1075-825 µm and the grayscale of the images. In addition, the Archimede flow loop has been upgraded with the installation of AE sensors, a permittivity probe and a high-speed camera.

Acoustic emission testing

Acoustic emission is the appearance of transient acoustic waves due to a change in the system. For the experiments carried out in the Archimede flow loop, it captures the acoustic emission produced by the flow inside the pipe, as part of the energy dissipated due to shear and particle-particle and particles-wall collisions is converted to acoustic energy. The appearance of hydrates is expected to produce more noise than before hydrate formation. Each sensor is fixed outside of the pipe and it is a non-intrusive measurement, as illustrated in Figure 2. The acoustic waves captured by a sensor can be converted to absolute energy, amplitude, counts and many other parameters. For this work, it is analyzed only the absolute energy, which is measured each 0.5 second. sensor with a frequency of 350 kHz. The absolute energy is defined as the integration of the output voltage of the transducer over time during an acoustic emission hit divided by the electrical resistance of the measuring circuit (Cameirao et al., 2018). The parameters of the AE acquisition system chosen for the tests are indicated in Table 1. The experiments are conducted at 4 °C and 75 bar (absolute pressure), using the following mixtures:

• Kerdane oil (C11 -C14 hydrocarbons).

• Saline water, with a concentration of 30 g of NaCl/L of deionized water.

• Natural gas (methane 91.7%, ethane 5.9%, propane 0.6%, carbon dioxide 0.8%, nitrogen 0.8%, i-butane 0.1%, n-butane 0.1%).

The volume of the liquid injected is 10.0 L at room pressure and temperature. Two tests were chosen among twenty-three for the analysis hereby presented: one at 30% water cut and another one at 80% water cut, at constant flow rate equal to 200 L/h.

Results and discussions

Detecting hydrate crystallization

For the first experiment, at 30% water cut and 200 L/h, Figure 3 shows the absolute energy for the four AE sensors, two temperatures, and the flow rate. The data from the different instruments are synchronized in time and space, meaning that the time it takes for a volume passing in a sensor to arrive to another sensor (dependent of the volume between the sensors and the flow rate) is considered for the plots. As indicated in the x-axis, the instrument used as reference is the Coriolis. One can see that, around 17.4 minutes, there is an increase of around two orders of magnitude in absolute energy for all the sensors (Figure 3(a)). This indicates hydrate formation, due to the collisions of particles that generates more noise, as previously observed by Cameirao et al. (2018). This is confirmed in Figure 3 The multi-instrumental methodology hereby presented allows detecting the position in the flow loop where there was initially a significant hydrate growth. Figure 4 shows the temperatures before and after the pump and the absolute energies for all the AE sensors. The data now is not synchronized in space, in order to detect the spatial displacement of the hydrates. Around 16.6 minutes, the temperature T8 (see Figure 4(a)) starts increasing, accusing hydrates passing in the position where the sensor is installed (after the pump). Less than a minute later, around 17.6 minutes, Figure 4(b) shows that the sensor 1A presents a sudden increase on the absolute energy, and then one by one of the other AE sensors also react, in sequence, with an increase of absolute energy. Finally, the last probe to detect an increase of the temperature was the sensor T7, around 18.5 minutes, as shown in Figure 4(c). The results indicate that the position where hydrates initially formed was between the sensors T7 and T8. Indeed, the power provided by the pump may facilitate the local fluctuations of energy that allow hydrates to reach a stable size and grow. These results also confirm that the increase of the absolute energy is due to the hydrate slurry flow when hydrates pass in the position where each sensor is placed.

Figure 4. Detecting the onset of hydrate formation with temperature and absolute energy measurements for the test at 30% water cut and 200 L/h.

The AE system presents a great advantage compared to the temperature probes, which is its capacity to track the displacement of particles in space. From Figure 3(a) and Figure 4(b), it is clear that hydrates are not homogeneously distributed in space. Hydrate particles will concentrate in parts of the volume of the flow (regions at high absolute energy) leaving other parts only with liquid (values of absolute energy that are at the same level as before hydrates have formed). From the temperature measurement, it is possible to detect hydrate formation. However, the heat generated by the exothermal process is transferred to the liquid, preventing the detection of the displacement of hydrates by means of the temperature measurement, and it is not possible to detect the local accumulations of hydrates in parts of the flowing volume.

Figure 5 shows the data of PVM, FBRM and absolute energy when hydrates are initially detected.

The data is synchronized in space. The first PVM image to show hydrates is image 3 in Figure 5 The second peak observed on the absolute energy coincides with the arrival of the hydrates seen in the PVM (images 4-8). It is likely that there was also deposition on the FBRM instrument, since the number of counts remains approximately constant after the detection of hydrates (between 19.5 and 20.5 minutes), and it slightly decreases beyond 20.5 minutes.

The results indicate that immediately after hydrate formation, the phases started separating. There are regions with hydrates (peaks in absolute energy) and regions with liquid (absolute energy with the same values as before hydrate formation). This was previously observed in flow loop tests by [START_REF] Palermo | Pilot Loop Tests of New Additives Preventing Hydrate Plugs Formation[END_REF]. One reason for this phase separation is the tendency of hydrates to trap the water phase, the sponge approach, proposed by [START_REF] Bassani | A Multiscale Approach for Gas Hydrates Considering Structure, Agglomeration, and Transportability under Multiphase Flow Conditions: I. Phenomenological Model[END_REF]. Another reason is the drift velocity between hydrate particles and liquid (the hydrates are slower than the liquid phase). Moreover, another reason can be the loss of kinetic energy of particles due to collision with the walls, or collision between particles.

This mechanism is known as sloughing, which causes temporary accumulations due to non-permanent attachments of the particles to the wall. A combination of these three mechanisms is likely to be the reason for the separation of phases identified after hydrate formation, which forms a heterogeneous suspension, where some regions have more hydrates and other regions have liquid single-phase flow.

Despite the FBRM and PVM allow deducing many mechanisms of hydrate formation under flow [START_REF] Melchuna | Topological modeling of methane hydrate crystallization from low to high water cut emulsion systems[END_REF], the use of AE presents the important advantage of tracking hydrate particles during the experiment, which cannot always be possible with the FBRM and the PVM. Since hydrates are porous structures, they may easily stick with surfaces of the instruments, and then they reduce the visibility of the images and may possibly form deposits in those intrusive instruments, affecting their measurements. For the experiment at 80% water cut it is shown in Figure 6(a) that, the absolute energy increases continuously between 3 and 10 minutes, presenting some small oscillations. During the same interval, the flow rate is perturbed, as shown in Figure 6(b), which indicates a significant hydrate formation during this interval. Then the oscillatory behavior with the flow rate reduces, while the absolute energy starts presenting an intermittent behavior with large values followed by low values of absolute energy. Such intermittence presents a period of approximately 2.5 minutes. To better understand how the AE signals responded to hydrate crystallization for the test at 80%

water cut, the data from the first 20 minutes of the experiment is shown in Figure 7. The first hydrates can be seen in image 2, from the PVM-images in Figure 7(a). Once hydrates form, the absolute energy (Figure 7(c)) gradually increases. The absolute energies from all the sensors present oscillations between 3 minutes, which is the onset of hydrate formation, and 11 minutes. This interval coincides with the small particle suspension seen in the PVM-images 2-4. In addition, between 3 and 11 minutes, there was no detectable increase on the temperature (Figure 7(d)), meaning that the volume of hydrates formed was not enough to increase the temperature. A change in the flow regime occurred around 11 minutes, where larger hydrate particles can be seen with the PVM. The oscillations on the absolute energy decrease with the increase of temperature. For this experiment, the initial hydrate formation, which occurred at microscopic level, was not detectable by an increase of the temperature, but it was observable from the PVM images and from the absolute energy, showing a gradual hydrate growth until 11 minutes. After that, particles of larger sizes are detected (images 5 e 6 in Figure 7 ii. As the flow continues circulating around the loop, small hydrate particles break and detach from the oil surfaces. One can notice that because the image 3 becomes darker, even regions where it is not possible to see particles with a defined shape (bottom of the image). During this step observed in image 3, the absolute energy increases once again, and it starts oscillating more than during the previous step, which indicates the continuous hydrate formation and breakage of particles.

iii. The flow becomes more stable, with relatively large particles (several millimeters, that can be seen in the image) dispersed in the liquid. This can be seen in last image (image 4, Figure 8 . The volume of hydrates formed is calculated from the gas consumed for hydrate formation, obtained from the gas injected in the system to maintain the pressure constant.

For the test at 30% water cut, the absolute energy for the sensor 2B during two turns around the loop is shown in Figure 9. The flowing volume calculated during each one of the two intervals was approximately 7.39 liters. Considering that the final volume of liquid with hydrates in the system is 10.72 liters, the estimated non-flowing volume is 31.1%.

Conclusions

An application of AE technology in flow loop tests with gas hydrates formation under multiphase flow was presented in this work. It was shown that the absolute energy captured by a sensor increases as hydrates flow in front of the sensor. Consequently, it can be used to track hydrate particles in space. AE presents some advantages compared to other instruments. First, it is non-intrusive, and therefore, it does not disturb the flow or cause any local accumulations. AE is also more sensitive to crystallization than the other probes tested, especially the temperature. In addition, it allows differentiating regions of the liquid flow with or without hydrates. The other instruments do not allow following the hydrate particles in space.

AE enables detecting and quantifying the non-flowing volume due to deposition or local accumulations in singularities. This is possible by monitoring the turns around the loop and measuring the interval of time for each turn. By integrating the flow rate over time, it is possible to calculate the total flowing volume, and consequently, the fraction of the mixture that does not flow.

An important observation was that the flow pattern rearranges after hydrate formation in a way that there is a separation into two main regions: one with liquid and another one with liquid and hydrates. The hydrate particles accumulation in parts of the liquid volume can be detected by AE, forming large pockets containing hydrates and entrapped liquid, which may be an important cause of plugging. This mechanism will be further investigated and explained in future works. Furthermore, advancements in the application of this technique with AE will further allow quantifying the local hydrate fractions in the system.

Figure 2 .

 2 Figure 2. Acoustic emission sensor installed in the Archimede flow loop. The position where the AE sensors are installed in the system are indicated in red in Figure 1. Four sensors were used (1A, 2B, 3C and 4A) in the tests presented. For each sensor, the number indicates the position in the system, while the letter indicates the nominal frequency: the type A are R15 sensors with a frequency of 150 kHz, type B is a PKBBI sensor with a frequency of 300 kHz, and type C is a WD

  (b), where the temperatures T8 and T9 increase around 18 minutes as a result of the exothermal process of hydrate formation. Hydrate formation provokes an oscillatory behavior in the flow rate, shown in Figure3(c), when particles are suspended in the system. The flow rate also indicates that the system is no longer flowable around 30 minutes, when it is equal to zero, indicating plug. It is possible to see that the plugging was a sudden event because the flow rate was relatively stable at 200 L/h before became zero instantaneously. The plug was caused by the presence of large pockets containing hydrates while other parts of the flow contain only liquid. These pockets with hydrates are characterized by relatively large values of absolute energy, as observed in Figure3(a), between the instants 20 and 30 minutes, while the regions with only liquid have the same level of absolute energy as before hydrate formation.

Figure 3 .

 3 Figure 3. Experiment at 30% water-cut and 200 L/h, showing the: (a) absolute energies, (b) temperatures T8 and T9, and (c) measured flow rate.

  (a). These particles seem to be flowing dispersed in the liquid phase (dark background), while images 4-8 indicate that there are relatively large porous agglomerates passing in front of the PVM. The images 5-7 are very similar, which indicates that there was deposition on the instrument. The passage of the first particles (image 3) was not detected by the FBRM (Figure5(b)), but it could be detected from the absolute energies (Figure5(c)), which have a first peak when the particles seen in image 3 pass near the AE sensors.

Figure 5 .

 5 Figure 5. Onset of hydrate formation for the experiment at 30% water cut and 200 L/h, comparing (a) the PVM images, (b) chord counts and PVM-brightness, and (c) absolutes energies.

Figure 6 .

 6 Figure 6. Experiment at 80% water-cut, 200 L/h, showing the: (a) absolute energies, and (b) flow rate.

  (a)), which can be confirmed by the grayscale of the PVM images shown in Figure 7(b). The chord counts for the group 100-1000 µm also increased around the same time, around 11 minutes.

Figure 7 .

 7 Figure 7. Detection of hydrate formation for an experiment at 80% water-cut by comparing the (a) PVM images, (b) chord length counts with PVM images grayscale, (c) absolute energies, and (d) temperature.

Figure 8

 8 Figure8shows some images from the camera compared to the absolute energy captured from the AE sensor installed near the visualization window. Based on both information, it is possible to separate the beginning of hydrate formation and growth into three the different steps: i. Once hydrates start forming, they form on oil surfaces. One can notice that because the droplets become darker once hydrates start forming (image 2, Figure8(a)), when compared to the image of the flow before hydrate formation (image 1). This beginning of hydrate formation causes a slightly increase on the absolute energy (before the instant 6.5 minutes, Figure8(b)).

  (b)), showing the flow regime once the small oscillations of absolute energy are over.

Figure 8 .

 8 Figure 8. Comparison between the images from the camera and the absolute energy measured near the window for the test at 80% water-cut and 200 L/h.

Figure 9 .

 9 Figure 9. Absolute energy during two laps in the flow loop for the test at 30% water cut. Applying the same procedure for the test at 80% water cut, Figure 10 shows the absolute energy from the sensor 3C during two laps around the loop. The flowing volume during those two intervals was approximately 8.28 liters, indicating that the non-flowing volume is around 1.95 out of 10.23 liters. It corresponds to approximately 19.0 % in volume of deposition or local accumulations.

Figure 10 .

 10 Figure 10. Absolute energy during two laps in the flow loop for the test at 80% water cut.

Table 1 .

 1 Characteristics of the AE sensors and acquisition.

		Instrumentation	Characteristics and setup
	Number of channels required for the test	4
		Sensors type	R15, PKBBI, WD -Physical Acoustic Corporation
		Resonant frequency	R15 (A): 150 kHz, PKBBI (B): 300 kHz, WD (C): 350 kHz
		Analog filter	20-1000 kHz
		Preamplifiers	30 dB
		Acquisition time step	0.5 s
		Peak definition time	200 µs
		Hit definition time	800 µs
		Hit lockout time	1000 µs
	2.3	Experimental conditions and materials

AKNOWLEDGEMENTS

The authors would like to acknowledge the financial support received from Total Energies in the framework of the project Archimede 5. We also would like to thank the technical support provided by members of the SPIN center at Mines Saint-Etienne, especially Mr. F. Chauvy and Mr. H. Faure for the technical assistance with the flow loop apparatus.