bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sand Management Practices for Extended ESP Run Life, a Study Case from a Mature Field in Ecuador
Authors: Xu Xianglin , Albuja Alexander , Izurieta Alvaro , Wan Xuepeng , Yang Hua , Wang Zhengwen 
Published: March 2022

Abstract
To analyze some factors that affect electrical submersible pump (ESP) performance and its reliability, focused on sand control practices (downhole devices as well as chemical sand consolidation), fluid characterization, and bending stress effects. To describe some study cases where these practices were applied over the last ten years leading to an increase in ESP reliability. To show ESP performance for 74 installations executed during the latest development stage in the Tarapoa block.
ESP performance in Tarapoa is affected by downhole devices sand production efficiency such as screens, automatic solids fallback preventer valves, and chemical sand consolidation treatments, the study starts with a summary of these experiences and related issues. Viscosity influence on ESP performance is studied since design stage sensitivity to water cut, emulsion, API gravity, and sand content is included. During the production stage, the need for single, double, or multiport capillary systems is described as well as the chemicals used. The finite element method is used to evaluate the stress effect during ESP installation and optimal setting depth for different well trajectories.
The best method for sand control in directional wells is chemical sand consolidation, this technique does not require additional downhole equipment and the effect on the productivity index is negligible. Standalone screens are recommended for horizontal wells but their effectiveness in sand production is limited. Automatic solids fallback valve provides some protection to ESP by preventing sand deposition when well is shut-in, up to 2,320 days running with this equipment. The production status of ESP under different viscosity conditions is simulated improving ESP run life. In addition, chemical injection effectively improves the physical properties of the fluid, which makes ESP relatively stable. Through the application of finite element software, a reliable stress distribution through the equipment was obtained. This analysis is used during installation operation and to define recommended setting depth. Average ESP run life extended over 1,900 days with a Mean Time Between Failure (MTBF) over 4,000 days.
This study summarizes the practices used to improve ESP performance over the last 10 years. ESP performance is affected by various factors that are described in this study from downhole sand control devices, chemical treatments, accessories installed along with ESP, and chemicals used during the production stage.
A New Insight into the Determination of True Residual Oil Saturation and Oil Relative Permeability from the Experimental Data in Heavy Oil/Water Systems
Authors: Esmaeili Sajjad , Kantzas Apostolos , Maini Brij 
Published: March 2022

AbstractDetermination of true residual oil saturation and oil relative permeability curve for heavy oil/water systems requires extensive effort and time as the breakthrough time occurs early in the imbibition process and the history match techniques are not able to obtain these two parameters perfectly. The aim of this work is to provide a new insight into the determination of residual oil saturation and oil relative permeability from core flooding in heavy oil/water systems at different temperatures.Literature claimed that the ratio of water relative permeability to oil relative permeability should be considered besides the production and pressure drop data in history matching to determine the residual oil saturation more accurately. In this regard, different relative permeability curves from our previous experimental works are incorporated in a series of simulations that were run for up to 100 PV of water injection. Production and pressure drop data were generated where a normal error distribution is added to the input data. The history matching runs (considering relative permeability ratio) are carried out to examine how many pore volumes of water need to be injected to reach the true residual oil saturation accurately in different experiments.The history matching results (with a Corey relative permeability model), employing the production data, pressure drop data, and the ratio of water relative permeability to oil relative permeability, which can be calculated fairly accurate from the Welge method, confirm that water relative permeability exponent is generally independent of the volume of injected water. Since the irreducible water saturation can be determined nicely during the oil flooding due to the inverse mobility ratio, the water relative permeability is not a function of volume of injected water. In contrast, determination of the true residual oil saturation in five experiments out of six is predicted with an error less than 5%. For several systems at different temperatures, at least 40 PV of water needs to be injected to result in accurate residual oil saturation determination. The oil exponent determination reveals a deviation of 20%-60% from the entered value to the simulation. The ratio of water relative permeability to oil relative permeability should be inserted into the simulation for acceptable history matching of relative permeability determination.The estimation of true residual oil saturation for relative permeability determination from the experimental data cannot be achieved easily. In this study, a new technique described by in the literature has been examined and tested to determine the required pore volume of injected water in different heavy oil/water systems within a wide range of temperatures.
Machine Learning Based Integrated Approach to Estimate Total Organic Carbon in Shale Reservoirs – A Case Study from Duvernay Formation, Alberta Canada
Authors: Sharma Gaurav , Hayes Derek 
Published: March 2022

Abstract
Shale gas reservoirs have become prominent contributors to the world's hydrocarbon resources and production. They exhibit multiple storage mechanisms, two of which are linked to the free and adsorbed gas phase. Since the adsorbed gas may be stored as a denser phase than the free gas, the contribution of the adsorbed phase can be significant. The adsorbed volume is related to the total organic carbon (TOC) and thus, higher TOC can indicate higher hydrocarbon inplace. Furthermore, productivity can be linked to TOC through the potential for overpressure and conversion of kerogen to pore space. However, estimation of the TOC is not a trivial problem, as it depends on geological factors such as depositional environment. In this study, we propose an integrated workflow using concepts of machine learning to estimate TOC.
The workflow is divided into 3 sections which are area selection, sub-region categorization, and prediction modeling. Firstly, 3 active exploration and development areas (Kaybob, Pembina, and East shale basin) of the Duvernay Formation are highlighted and the geology of each specific area is analyzed. Thereafter, using the available core data and average properties of the attributes (Gamma Ray, resistivity, density, and distance from mean vitrinite reflectance line), each area is clustered into sub-regions using SVM, logistic regression, and k-means clustering. Finally, using Random Forest prediction, models for each sub-region are developed and ranked with average mean square errors and standard deviations.
It is observed that the Kaybob area can be clustered into 2 regions. This observation is supported by the principal component plot (PC1 vs PC2), which shows a dual cloud structure. This is further supported through clustering analysis, which also revealed the same observation. Results of the prediction modeling found random forest as the best predictor, achieving a match wiht the core data with a error less than 10% and in some cases only a 1% deviation.
Shale reservoir characterization requires estimation of the key parameters such as TOC. However, it is difficult to estimate TOC with purely physics-based or purely statistical models, as it requires limited specialized data and is impacted by subtle variations in the reservoir. This study suggests that TOC can be accurately estimated by combining geological interpretation and machine learning based algorithms without bearing cost of the specialized data.
Method for Drawdown Analysis of a Multi-Stage Hydraulically Fractured Horizontal Well That Penetrates an Unconventional Naturally Fractured Reservoir
Authors: Gutierrez Oseguera Alejandra , Aguilera Roberto 
Published: March 2022

AbstractThis paper examines the pressure response of a horizontal well that penetrates an unconventional, naturally fractured reservoir. The response is quite surprising. The expectation of linear flow is shattered, and only radial flow is observed. The radial flow two parallel straight lines in a semilogarithmic crossplot of flow pressure vs. time are present but they are reversed, with the last straight line showing smaller pressures as compared with the extrapolated first straight line.Two different methods are used; the first one is a conventional approach for analyzing the first semilog straight line with a view to calculating flow capacity and permeability well as skin. The second approach involves a novel dual porosity model that permits calculating several fracture parameters of interest, and to the best of our knowledge has not been published previously in the petroleum engineering literature. In this paper, new equations with a semi-empirical component, are presented that allow matching the reversed real pressure drawdown data as well as the corresponding pressure derivatives.The new model shows that fluid flow is dominated initially by the fractures as in the case of dual porosity conventional models. In the conventional model, flow pressure data deviate from the first straight line toward the right due to pressure support stemming from fluids that move from the matrix toward the fractures. Eventually, a pressure equilibrium is reached and a second straight line, parallel to the first one, is developed. However, in the case of the model presented in this paper the data deviates, not to the right of the first straight line, but down and below the first straight line. This pressure drop is interpreted to be the result of boundary-dominated flow. Next, a pressure equilibrium is reached between matrix and fractures, and the last line becomes parallel to the first straight line. It is shown that correct pressure and derivative matches permit estimating various parameter of interest such as size of the matrix blocks, number of fractures that intercept the well bore, storativity ratio omega, partitioning coefficient (the ratio between fracture and matrix porosity), matrix permeability, and the ratio of fracture to matrix hydraulic diffusivity.The novelty of this study is the development of a new easy-to-use well testing model for matching an unconventional pressure response during drawdown of a horizontal well that penetrates an unconventional tight dual porosity reservoir. The new method is explained with a step-by-step example that uses real data from the giant unconventional Chicontepec paleochannel in Mexico and can be reproduced readily by the reader.
Multiscenario Development Planning by Means of the Digital Twin of the Petroleum Field
Authors: Aslanyan Artur , Popov Arkadiy , Zhdanov Ivan , Pakhomov Evgeniy , Gulyaev Danila , Farakhova Rushana , Guss Rodion , Dementeva Mariya 
Published: March 2022

Abstract
This paper presents a practical case of Field Development Planning (FDP) process with extensive use of petroleum asset digital twin facilities. The paper explains the process of setting up both the digital twin and the performance metrics which were used to steer the multivariate trials on redevelopment activities towards the optimal investment scenario.
The petroleum asset is represented by a block of the large oilfield in Western Siberia with ongoing waterflood project at mature stage.
The process of building FDP was performed through a series of interactive sessions with the petroleum asset digital twin which includes three major group of functionalities:
Convert redevelopment activities (drilling, workovers, production optimization and surface facilities) into the production response and basic investment indicators including NPV, PI, IRR, MIRR, ROI Provide technical performance metrics (such as formation pressure, watercut and recovery responses, potential case of integrity failures, behind casing channelings, spontaneous formation fracturing, surface pipeline pressure losses) that can help understand the results of the FDP activities Provide well and cross-well surveillance simulations (pressure tests, production and integrity logging) to help identify the candidates for future monitoring.
Two different multidisciplinary teams undertook 12 FDP iterations over two different 3D full-field model realizations to arrive at the best investment scenarios for each model.
After that the FDP team has picked up the best practices from both FDPs in the form of those field development actions which turned to be financially successful in both model realizations. All those cases were prioritized and merged into the ultimate FDP scenario and verified across both digital asset realizations.
The new FDP suggested the new drilling opportunities, few integrity workovers, few conversions and a new production target strategy for producers and injectors. Apart from investment benefits, the new FDP provides substantial accelerated oil withdrawals and increase in ultimate recovery comparing to the no-future-activity scenario.
Integrating Domain Knowledge with Machine Learning to Optimize Electrical Submersible Pump Performance
Authors: Sharma Abhishek , Songchitruksa Praprut , Sinha Rajeev Ranjan 
Published: March 2022

Abstract
A data-driven workflow was developed to monitor electrical submersible pump (ESP) health using an anomaly detection method with high-frequency sensor data. The workflow would help maximize the run life of ESPs while reducing the cost of maintenance. The new workflow contrasts with conventional field maintenance which is often reactive and incurs additional downtime in logistics and inventory management in diagnosing the issues and taking the recommended actions. In contrast, using machine learning (ML) concepts can save operating costs, especially in the case of the ESPs widely used for artificial lift.
Many operators augment ESPs with high-frequency (HF) sensors to monitor their performance, but much of this information remains either unused or partially used in post-failure analysis. The application of ML concepts in understanding ESP operational behavior complements the existing domain practice. The workflow we describe in this paper begins with domain knowledge and exploratory statistical analysis to find the key performance indicators (KPIs) related to ESP failure. Feature engineering and advanced ML techniques are used to build and test healthy ESP models for each selected KPI. Multiple health signals are fused to improve the performance of anomaly detection using historical ESP failure data and pullout reports as benchmarks.
In a test of the workflow, the model was trained on the data from a group of active producing wells with reported historical events, failures, and pullout reports. The data contained several well events and several reported failures. This information was used to fine-tune the alarm thresholds for the health indicators. The model was able to detect approximately 70% of failure events (true positive rate) in the data set. The false alarm rates for the configured model were approximately at 20% (false positive rate). The solution can be implemented in a dashboard to monitor ESP KPIs and show health alarms. These alarms can be further prioritized based on the failure probability and remaining useful life of the ESP. The health signal degradation patterns can be captured and learned to predict the remaining useful life of the ESPs, thus enabling operators to allocate and prioritize maintenance resources. In addition, the analysis of ESP pullout reports can provide insight into the relationship between health signals and root causes of the failure, which can be structured into a formal Bayesian network to provide automatic root cause interpretation
The data-driven approach takes advantage of the vast amount of reservoir, production, and facilities data and provides insights into nonlinear multidimensional relationships between parameters to better understand and optimize field development and to adopt a proactive approach toward equipment maintenance.
A Model Ranking Approach for Liquid Loading Onset Predictions
Authors: Jia Hao , Zhu Jianjun , Cao Guangqiang , Lu Yingda , Lu Bo , Zhu Haiwen 
Published: March 2022

Summary
As a natural gas well ages, liquid loading is frequently encountered, leading to the decrease of gas production rate and many other side effects, which may in turn cease the gas production. Thus, to accurately predict liquid loading onset is of significant importance in gas wells for the sake of stable production. With years of research and development in the natural gas industry, the liquid loading onset prediction models prevail in the existing literature. Based on varying mechanisms (e.g., droplet falling back, liquid film reversal, etc.), the critical gas velocities or flow rates corresponding to flow pattern transitions in gas wells can then be calculated. However, a universally validated model, whether empirical or non-empirical, that is applicable to predict the onset of liquid loading in versatile gas wells conditions (e.g., horizontal, vertical, and inclined) is, as yet, still unavailable.
In this paper, we conduct a complete literature review and investigation of these existing liquid loading onset prediction models. First, we obtained detailed information of more than 600 gas wells, including well geometries, gas properties, operation conditions, and so on, from different gas fields. Then, we evaluate the validity of various liquid loading onset prediction models by use of a novel model ranking approach. To fully account for the effects of gas well properties (including but not limited to production, wellhead pressure, and pipe diameter) to the model prediction accuracy, the proposed method in this paper employs data clustering and normalization techniques, as well as the statistical relative error analysis, to rank and select the best suitable model for each specific gas well. Extensive comparison and verification of the ranking approach indicate that the proposed method provides a good reference for the rational production allocation and stable production of gas wells.
Experimental Investigation of Low-Concentrated Nanocomposite Polymer Gels for Water Shutoff Treatment Under Reservoir Conditions
Authors: Sheidaie Ali , Fahimpour Jalal , Sharifi Mohammad 
Published: March 2022

Summary
Excessive water production is one of the main challenges in mature oil fields. Applying polymer gels has proven to be an effective chemical treatment to this problem. Because of some limitations in application of polymer gels in oil reservoirs with harsh conditions, using nanoparticles (NPs) in gel structure has been proposed recently to improve the performance of such systems. However, while main body of the previous studies has only focused on improvement of bulk properties of polymer gels in presence of NPs, there is a serious lack of data on their performance in porous media. In this study, we have conducted a comprehensive investigation on application of two new nanocomposite polymer gels through bulk-gel static tests and dynamic core displacement tests. Fe2O3 and NiO NPs at low concentrations (&lt;100 mg·L−1) in AN125VLM/chromium acetate polymer gel system were used. The results of bulk bottle tests at 82℃ demonstrated that the nanocomposite gel systems have the same gelation time and gel strength as the NPs-free gel. Swelling ratio measurements were also performed in a high-salinity formation water (268 949 mg·L−1), and accordingly, while NiO NPs deteriorated the swelling behavior of the gel system, Fe2O3 NPs showed a positive impact. The results of the strain-sweep test demonstrated relatively similar linear viscoelastic region and thus the same gel strength for all gel samples. In addition, the results of the thermogravimetric analysis (TGA) revealed that Fe2O3 NPs had a minor positive impact on thermal resistance of the polymer gel system. Finally, high pressure-high temperature coreflooding experiments were carried out on three sandstone reservoir core samples. Although the NPs-free gel showed a desirable level of disproportionate permeability reduction, the injection of Fe2O3 nanocomposite gel showed unsatisfactory results and caused rock permeability degradation. The pressure drop trend and deposition of polymer gel particles at the upstream injection side confirmed the aggregation and plugging effect of NPs during injection. Our findings demonstrate the critical aspect of dynamic core displacement testing of nanocomposite gels before their use for field applications.
SNPs in Apolipoproteins Contribute to Sex-dependent Differences in Blood Lipids Before and After a High-fat Dietary Challenge in Healthy U.S. Adults
Authors: Wang Yining E. , Kirschke Catherine P. , Woodhouse Leslie R. , Bonnel Ellen L. , Stephensen Charles B. , Bennett Brian J. , Newman John W. , Keim Nancy L. , Huang Liping 
Published: March 2022

Abstract
Background: The effect of genetic polymorphisms on fasting blood lipid levels have been widely studied but the effects of these within the context of a high-fat meal challenge remain less characterized. The current study aimed to investigate the association of SNPs in lipoprotein-related genes with blood lipid profiles in healthy adults in the U.S. Methods: Subjects (n=393) between 18-66 years of age with BMIs ranging from 18.5-45 kg/m2 completed the cross-sectional Nutritional Phenotyping Study. Among them, 349 subjects (men: 48%; women: 52%) gave consent for genotyping. SNPs in APOA5, APOB, APOC3, APOE, and LDLR were assessed. The association between lipid markers and genotypes was tested separately for each SNP with analysis of variance (ANOVA), adjusted for sex, age, and BMI. We also examined two-factor interactions between SNPs and sex, age, or BMI. Results: Women carrying the C allele of rs3135506 in APOA5 or men carrying the C allele of rs429358 in APOE had reduced HDL-cholesterol levels during fasting and postprandially. The C allele in APOE was also correlated to increased LDL-C levels. The TT genotype of rs2854116 in APOC3 was associated with elevated total cholesterol. Additive effect of the risk alleles of APOA5 and APOE or APOC3 and APOE was detected. Nevertheless, the tested SNPs had little impact on the postprandial triglyceride responses to the high-fat challenge meal. We found no significant effects of SNPs in APOB (rs1042034) or LDLR (rs2228671) on triglycerides, cholesterol, or free fatty acid levels.Conclusions: In healthy adults, fasting and postprandial cholesterol levels are strongly correlated with the tested APOA5, APOE, and APOC3 genotypes. Sex attributes to the genetic impact of the tested SNPs on lipid profiles.Trial registration: ClinicalTrials.gov, NCT02367287. Registered February 20, 2015, https://clinicaltrials.gov/ct2/show/NCT02367287.