bluuebunny/crossref_metadata_2025_split huggingface dataset:
MWD Tools’ Electronic Components Data-Driven Failure Detection
Authors: Rodriguez A. , Rey-Torres C. , Kohar A. , Malik A. 
Published: April 2024

Abstract
Within the oil and gas industry, measurement-while-drilling (MWD) tools have several sensors to provide telemetry data that allow performing either live or post-job health diagnostics of the tool components and effectively replace/repair them to avoid unnecessary nonproductive time. Nevertheless, health checks are often purely telemetry-based, not using past job information to quantify cumulative component degradation, which increases the probability of failure during subsequent jobs.
This work proposes a methodology that (1) introduces historical data to improve on failure detection and (2) benchmarks it against single-job-based models over the same features for a particular MWD tool. We find that by stacking historical features (as multivariate time series), failure prediction using one-dimensional convolutional neural networks provides better estimates of the probability of failure.
We apply our methodology to five of the most common electronic component failure detection models trained over the time series the telemetry channels of 38 coiled tubing drilling MWD assets, totaling 752 jobs, and present the comparison between single-job-based models (with an average F1-score 0.66) and historical data-based models (with an average F1-score of 0.75).
Geochemical Modeling of Low Salinity Polymer Flooding for Carbonate Rocks
Authors: Arellano Aaron Guadalupe Tellez , Al-Shalabi Emad W. , Hassan Anas M. , Kamal Muhammad S. , Patil Shirish , Hussain Syed M. Shakil 
Published: April 2024

Abstract
The demand for global energy has been continuously increasing, and the oil and gas (O&G) industry is a significant supplier of energy required to meet this demand. However, the industry faces challenges due to regulatory constraints and exploration complexity, which have made it necessary to maximize oil recovery from existing fields. Enhanced Oil Recovery (EOR) techniques have shown great potential in increasing oil recovery from reservoirs that were produced by conventional primary and secondary recovery methods. Chemical Enhanced Oil Recovery (cEOR) techniques, specifically Polymer flooding, have proven valuable in improving the macroscopic sweep efficiency and changing rock and fluid interactions at a reasonable technical and economic cost. To appropriately select a suitable polymer, it is essential to understand the impact of diffusion, polymer adsorption, and geochemical interactions between the polymer, brine, and rock phases.
The main objective of this study is to propose a mechanistic model that captures the physicochemical aspects of polymer flow in porous media through a geochemical perspective using a coupled reservoir flow and geochemical numerical simulator (MRST-IPhreeqc simulator) for applications in carbonate rocks. The study developed a mechanistic model using MRST reservoir flow and the IPhreeqc geochemical simulator, with the MRST polymer module modified to model key parameters such as polymer viscosity, adsorption, IPV, RRF, hydrolysis, and shear effects. Surface Complexation Modeling from IPhreeqc was integrated to model Indiana limestone carbonate rocks and introduced a polymer species of ATBS sulfonated polymer for interaction within the MRST simulator for Low Salinity Polymer (LSP) flooding paradigm. The adsorption equilibrium is captured through thermodynamic reactions and flow equations. The updated simulator was validated against experimental tests for carbonate rocks. The results prove the simulator’s effectiveness in modeling the main mechanisms of LSP. This study offers insights into geochemical, reservoir flow, and adsorption in polymer flooding. The integration of geochemical factors is crucial for optimizing polymer flooding in the Middle East’s harsh carbonate reservoir conditions, enhancing regional oil recovery.
A Reinforcement Learning Approach to Optimize CO2 Removal from Flue Gas in Carbon Capture Applications
Authors: Zirrahi M. , Santiago C. , MacFarlan K. 
Published: April 2024

Abstract
Reinforcement learning (RL) has gained interest in chemical process and production industries for control and optimization of the process. The RL algorithm does not need any kind of field knowledge to start the optimization process. The algorithm will be trained and adjusted during the learning and training steps. This paper presents an evaluation of the RL approach in the context of CO2 removal from flue gas for carbon capture applications.
A carbon capture plant is simulated using a commercial process simulator to be served as the RL algorithm’s environment. Observed values and control parameters required for plant optimization are defined and fed into the algorithm. These parameters were used for reward, policy, and state definitions. The agent in the RL model uses a deep deterministic policy gradients (DDPG) algorithm for process optimization. Two neural networks were used as the critic and actor for policy generation. The optimization target is the minimization of the required operating energy while removing the optimal amount of CO2 from the flue gas.
The RL algorithm was used to train the critic and actor networks toward the optimum policy for operating the carbon capture plant. The result of the project is an assessment of the capture plant response to the changes in CO2 composition and inlet gas flow rate. The goal of the plant operation is to minimize the required operating energy while removing the optimal amount of CO2 from the flue gas. The results of the trained RL algorithm have been evaluated under different operating conditions. The required energy was compared with the base case; i.e., the case with recommended operating conditions by the simulator vendor. The results showed that trained RL algorithm can operate the capture plant with lower energy costs in more than 70% of the cases. The paper presents an evaluation of the RL method for CO2 capture process optimizations in digital transforming and implementing artificial intelligence (AI) in oil and gas industries.
Fluid Migration Along Faults: Implications for CCUS
Authors: Zhou J. D. , Likanapaisal P. , Lun L. S. , Krishnamurthy P. 
Published: April 2024

Abstract
The capture and geological storage of CO2 is a safe, proven, and immediately deployable technology that can decarbonize heavy industry and manufacturing. Worldwide, there are several projects that currently store CO2 in depleted hydrocarbon fields and saline aquifers. In the United States, there has been significant progress in CCS project development in such formations subject to Underground Injection Control (UIC) Class VI guidance. Concern for contamination of underground source of drinking water (USDW) zones is regulated by limiting CO2 injection near faults. This represents a loss of potential pore space for CO2 storage.
This work examines fluid migration along faults using a model based on a potential storage site in the Gulf of Mexico (GOM). The feasibility of brine and CO2 migration from the storage zone into the upper layers of the formation and USDW zones via faults is investigated through numerical simulation. A fault plane is simulated through local grid refinement (LGR) with varying properties to imitate different fault transmissibilities. Additional sensitivity studies on various operating parameters and fault characteristics were performed to determine conditions that could lead to USDW contamination. The simulation followed an injection schedule of 30 years injection at 1.6 MTA followed by 1000 years of shut-in observation. Fluid tracers were implemented to track the migration of brine to and from varying zones within the model.
A range of fault characteristics, ranging from an open conduit to a fully sealing fault, were studied to observe the behavior and migration of fluids over the course of the simulation to get a sense of what average fault permeability is necessary to get a material amount of fluid migration. More realistic variations of fault characteristics will result in more conservative cases of CO2 migration upwards toward the USDW zone. Future work will involve extending this work to additional assets and using more sophisticated fault permeability descriptions.
Machine Learning Models to Predict Production Rate of Sucker Rod Pump Wells
Authors: Thabet S. , Zidan H. , Elhadidy A. , Taman A. , Helmy A. , Elnaggar H. , Yehia T. 
Published: April 2024

Abstract
The design, operation, and optimization of Sucker Rod Pumping (SRP) systems necessitate the utilization of production data. However, forecasting fluid flow rates at the surface of SRP artificially lifted wells usually poses a challenge, especially in instances where traditional separators and multiphase flowmeters are not universally available. Consequently, this study introduces nine machine learning (ML) models employing real data sourced from 598 wells with a production history exceeding three years. The dataset, comprising 8,372 data points, undergoes a random split allocating around 80% of the data (6,697 data points) for training, while around 20% (1,675 data points) are used for testing. The ML models encompass Gradient Boosting (GB), Adaptive Boosting (AdaBoost), Random Forest (RF), Support Vector Machines (SVMs), Decision Tree (DT), K-Nearest Neighbor (KNN), Linear Regression (LR), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD). The chosen input features for the models are readily accessible during any SRP well-lifting process, and these inputs include various variables such as wellhead flowing pressure, casing pressure, predicted bottom hole fluid production rate, predicted bottom hole oil production rate, net liquid head above the pump, pump size, pump clearance, stroke length, pump speed, pump setting depth, the temperature at the pump depth, oil gravity, and water viscosity. Evaluation of the different ML models’ performance is carried out by two methodologies: K-fold cross-validation, and repeated random sampling. The findings reveal that the top-performing models are GB, AdaBoost, RF, LR, and SGD, exhibiting mean absolute percentage errors of 3.6%, 3.4%, 3.4%, 4.0%, and 4.4% respectively, and correlation coefficients (R2) of 0.937, 0.934, 0.935, 0.921, and 0.915, respectively. In practical field application, these models are deployed on a well within Egypt's Western Desert fields, demonstrating excellent agreement between actual fluid rates and model predictions. In conclusion, across diverse pumping scenarios and completion configurations, the ML models could effectively forecast production rates for different SRP wells. This capability facilitates continuous monitoring, optimization, and performance analysis of SRP wells, enabling swift responses to operational challenges since the proposed ML models offer an accessible, rapid, and cost-effective alternative to conventional separators and multiphase flowmeters.
Deep Learning-Based Segmentation for Quartz Overgrowth Quantification in Geothermal Sandstone Reservoirs from Scanning Electron Microscope (SEM) Images
Authors: Sausan Sarah , Andaru Arkanu 
Published: April 2024

Abstract
This paper presents an update on a dynamic segmentation algorithm for detecting quartz overgrowths in geothermal reservoirs using Scanning Electron Microscope (SEM) images. Previously, the Random Forest algorithm had been employed in the automated workflow for quartz overgrowth detection from SEM images. A 75% accuracy score was achieved from the model training, indicating a promising start. This model was found to differentiate successfully between detrital quartz grains and their diagenetic quartz overgrowths; it was also demonstrated that it could identify porosity and other minerals.
A continuation of the algorithm development in the automated workflow is explored in this paper. Deep learning methods using U-net architecture is investigated to find the most fitting algorithm for detecting quartz overgrowth. The previously utilized texture-based feature extraction techniques are still incorporated. Normalization and dynamic overlaying algorithms are applied to address variations in image brightness and contrast and align BSE and CL images accurately, ensuring reliable segmentation. The segmentation process involves the coordination of BSE and CL images, utilizing their respective strengths, and overlaying them to achieve comprehensive results. This is followed by a two-fold model-building approach using separate segmentation models for BSE and CL images, which are then combined to distinguish between pore space, quartz grain, and quartz overgrowth.
The evaluation of the U-Net model's performance involves analysis of training and validation accuracy, loss, and intersection over union (IoU) over 50 epochs. Results demonstrate the model's capability to generalize and learn effectively, with the segmentation process showing proficiency in differentiating between the target mineralogy features. However, variability in performance across different datasets suggests the need for further model optimization.
In conclusion, the integration of U-Net into SEM image analysis for mineralogy detection represents a significant technological advance in geoscience, offering a more efficient, precise, and automated approach to understanding and exploiting geothermal energy resources. The findings also highlight opportunities for future research, such as exploring a variety of deep learning models, fine-tuning through transfer learning, and developing user-friendly tools for rapid mineralogy segmentation.
Effective Production Forecasting and Robust Rate Optimization Using Physics Informed Neural Networks
Authors: Meng Han , Zhang Ruxin , Lin Botao , Jin Yan 
Published: April 2024

Abstract
Waterflooding has a long history as a successful development strategy in oil recovery, yet maximizing its potential through optimized strategies remains a significant challenge. Traditionally, identifying the most effective waterflooding designs requires extensive numerical simulations, which can be computationally demanding. This study introduces a comprehensive framework that employs a Physics-Informed Neural Network (PINN) to optimize waterflooding designs for enhancing oil recovery. Specifically, the PINN incorporates fluid dynamics principles into deep learning algorithms and serves as a rapid surrogate method to effectively predicts oil production across a range of waterflooding scenarios. Furthermore, an optimization technique is designed to fine-tune injection designs, thereby optimizing oil recovery. Experiments on 2D synthetic and 3D Brugge benchmark cases demonstrate that the PINN model achieves higher accuracy compared to a pure data-driven neural network. Using the PINN surrogate, a genetic algorithm quickly searches the injection parameter space for optimal oil production. The optimized strategies are validated using the full numerical simulator, confirming the feasibility of the proposed approach. Overall, the integration of domain knowledge into deep learning not only improves the generalise ability of the pure data-driven model but also provides insightful physical interpretations for engineers.
Numerical Investigation of CO2 Convective Transport in Stochastically Generated Heterogenous Media: Implications for Long-Term CO2 Sequestration in Saline Aquifers
Authors: Shahriar Md Fahim , Khanal Aaditya 
Published: April 2024

Abstract
Dissolution trapping is one of the most dominant mechanisms for CO2 storage in subsurface porous media saturated with brine. The CO2 dissolution rate and overall fluid flow dynamics in subsurface formations can vary significantly based on permeability variation. Although some numerical simulations have focused on these factors, detailed flow behavior analysis under nonuniform permeability distribution needs further study. For this purpose, we conduct simulations on the flow behavior of CO2-dissolved brine in two different heterogeneous media. The spatial permeability variations in the cell enable the analysis of complex subsurface storage phenomena, such as changes in finger morphology and preferential dissolution path. Finally, the amount of CO2 dissolved was compared between each case, based on which we draw informed conclusions about CO2 storage sites. The results demonstrated a preferential movement of CO2-dissolved regions toward high permeability regions, whereas a poor sweep efficiency was observed due to minimum dissolution in areas with lower permeability. Furthermore, simulation results also reveal uneven CO2 concentration inside the convective fingers. This study provides fundamental insight into the change in flow behavior at heterogeneous regions, which could be translated into saline aquifer conditions. The proposed workflow in this study could be extended further to analyze complex heterogeneous storage systems at different flow regimes.
Improving ESP Production for Unconventional Wells Through Real-Time Machine Learning Based Changepoint Detection
Authors: Hamedi Shokrlu Y. , Bazile J. 
Published: April 2024

Abstract
The operation of unconventional wells utilizing electrical submersible pumps (ESPs) is filled with challenges such as broken shaft, intake blockage, blockage of pump stages, increase of free gas at intake, etc. Additionally, any deviation from optimal operation conditions means loss of production for operators. Detecting these events and conditions early is imperative for minimizing downtime and optimizing production. Most operators rely on manual and/or rule-based processes for the detection of these events, leading to delayed event detection and associated productivity losses. In this work, a novel approach is proposed for earlier detection of these events, which are referred to as changepoints.
Most of these changepoints can be identified as repeated patterns on the time series data collected during the well operation. Hence, a machine learning (ML) model trained on well historical data should be able to detect these changepoints. In this work, historical data from over 200 Permian unconventional ESP-operated wells are used for training and evaluating the ML models. This data includes high- frequency pump measurements, intake pressure, electrical current, and temperature as well as production rates.
Most of the changepoints show very similar changes in well data when comparing one well to others in the asset. Therefore, the objective is to develop a global model applicable to all the wells.
After labeling the known changepoints, different machine learning models are developed and evaluated. The sliding-window technique was leveraged for creating the training data. Various window sizes were tested. Statistical attributes of the data within each window were used as features for ML model training. These features include mean, standard deviation, and time series similarity metrics such as dynamic time warping, cross-correlation in the time/frequency domain, and power similarity.
Out of the different tested algorithms, the Extreme Gradient Boosting algorithm proved to be the most successful in detecting the changepoints. The trained model proved to be able to successfully identify close to 90% of the changepoint events in less than a few hours (compared to a few days or weeks of detection time using the manual monitoring processes) from the time of occurrence. For some events, the models were able to identify earlier data patterns that were leading to the main problem events hours before they happened.
Although a similar method is applied in other industries, this work is the first application of ML for ESP wells monitoring and changepoint detection. This work proves that ML models can lead to early detection of well problems, which helps operators take corrective actions before they cause disruptions and downtime, reducing repair costs and improving efficiency.
Prediction of Surfactant Performance from Surfactant Huff-Puff in Carbonate Reservoirs Using a Data-Driven Approach and Desktop Application Development
Authors: Yao Y. , Wei M. , Cui Y. , Ali M. , Leng J. , Qiu Y. 
Published: April 2024

Abstract
Surfactants have been widely used to alter the wettability of carbonate rocks from oil-wetness to water-wetness and enhanced oil recovery. One of primary methods implemented in filed applications for enhanced oil recovery is surfactant huff-puff. Currently, a large number of surfactant treatments are conducted in laboratories prior to field applications to optimize the design of surfactant huff-puff, test the performance of surfactants, and minimize failure risk. This process is time-consuming since a treatment could last from several days to more than 300 days. Moreover, a fraction of treatments could not improve oil recovery as reported in the literature. In this paper, we provide a machine learning based solution to improve this process. A comprehensive dataset that systematically compiles project data on surfactant treatments in carbonate reservoirs is constructed. Based on this dataset, machine learning models are developed to forecast incremental oil recovery resulting from surfactant treatments. Random forest model presents the best performance. This research could predict surfactant performance before a surfactant treatment is conducted, which could fasten laboratory investigation, save time and cost. Furthermore, an adaptive, offline, and friendly graphical user interface is designed to enable data analysis and assist in decision-making. With desktop application, it is easy to conduct data analytics and could be accessible for most engineers.
Experimental Investigation of Waterflood Performance Challenges in a Low-Permeability Morrowan Sandstone Reservoir: Implications for Mechanistic Modeling and Enhanced Oil Recovery Optimization
Authors: Morgan A. , Ampomah W. , Grigg R. , Wang S. , Czarnota R. 
Published: April 2024

Abstract
An indispensable factor to the development of improved enhanced oil recovery (EOR) is the in-depth insight into the fractional flow mechanistic effects of waterflood performance. Especially for an inefficient waterflood operated field. In this study the inefficiency of waterflood on FWU is investigated. The field is divided into two halves (East and west) of the same reservoir and similar geological characteristics. Though the east was prolific on primary recovery it failed on waterflood whiles the west performed efficiently and hence its undergoing WAG CO2-EOR improved recovery technique. Through core-flood experiments, the causative mechanisms are unraveled. X-ray diffraction (XRD), scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM/EDS) analyses were also conducted to assess mineral composition, pore characteristics and distribution. The core samples exhibited extremely low permeability, resulting in a drastic reduction in flow capacity. The reservoir primarily consists of quartz, clay minerals (kaolinite, illite, smectite), feldspar, and carbonates. Single-phase injection of low salinity water (SIIW) further reduced permeability and increased differential pressure compared to high salinity water (SCW). Higher flow rates caused significant increments in differential pressure, likely due to velocity-induced fine migration. pH changes and the presence of Al3+, Fe2+, and Zn ions indicated brine reactions. Two flood recovery strategies showed incremental recovery with SIIW injection with observed adverse effects. Fine migrations, clay swelling, scaling, and precipitation were identified as key causes of formation damage during low salinity water flooding. Element-mineral associations showed clays and mineral particles obstructing pores and pore throats. In summary, the principal mechanisms of waterflood inefficiency include low permeability, mineral composition (especially clay minerals), clay mineral reactivity, and increased pressure drop (ΔP). These factors collectively contribute to formation damage, pore plugging, reduced flow capacity, and ultimately the inefficiency of waterflood operations. These insights contribute to the development of effective waterflood strategies and improved recovery techniques for incremental recovery from the FWU-east field especially considering CO2-WAG EOR.