bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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.
Advancing Reservoir Understanding: Integrating Deep Learning into Automated Formation Micro-Imaging Log Interpretation for Enhanced Reservoir Characterization and Management
Authors: Gharieb Amr , Gabry Mohamed Adel , Elsawy Mohamed , Algarhy Ahmed , Ibrahim Ahmed Farid , Darraj Nihal , Sarker M. R. , Elshaafie Ahmed
Published: April 2024

The Formation Micro-Imager (FMI) produces high-resolution borehole images, crucial for identifying geological features like bedding and fractures, aiding in reservoir characterization. Its sensitivity to mineralogy and fluid content variations enhances its utility in subsurface analysis for optimizing hydrocarbon extraction strategies. However, interpreting FMI logs requires expertise and experience due to the complexity and nuances of the images, posing challenges in obtaining accurate geological information. This research harnesses the capabilities of automated Formation Micro Imager (FMI) interpretation combined with expert calibration to innovate the analysis of subsurface stress fields, striving to improve the accuracy of geo-mechanical modeling and optimize water-flooding projects, particularly those necessitating hydraulic fracture stimulations. It revolves around the creation of accurate local stress maps using FMI data, specifically drilling-induced fractures, to determine the present-day maximum horizontal stress (SHmax) orientations, thereby informing wellbore stability and drilling strategy optimization. This study utilizes advanced logging techniques to identify borehole breakouts and other enlargements, which are critical for accurate in-situ stress estimation. This process is supplemented by a comprehensive analysis of multiple wells, revealing the variability and predominant orientations of SHmax with the regional tectonic framework.

The study uncovers the significant variability of SHmax orientations, aligning predominantly with major fault trends within the Egyptian Western Desert's complex geological structure. The results from this analysis facilitate an updated regional stress map, indicating the prevalence of normal and strike-slip faulting regimes. Additionally, the research extends to reservoir management, where automated FMI log interpretation aids in optimizing hydraulic fracture directions in Field A, contributing to more efficient hydrocarbon extraction and better reservoir pressure maintenance strategies.

The novel integration of FMI data with deep learning applications presents a groundbreaking approach to subsurface analysis. It offers a new perspective on managing reservoirs by strategically placing water injector wells to support hydrocarbon production while maintaining reservoir integrity. This method enhances the predictive capability of simulation models, ensuring more accurate performance matching and better-informed decision-making in production strategies. The study's approach combines geological insights with advanced technology, offering a substantial advancement in the field of geo-mechanics and reservoir characterization.
Experimental Studies of Sulfonated Polymer Adsorption in Carbonates Under Challenging Conditions
Authors: Mahboob Ahmad , Hassan Anas M. , Khan Sarmad Z. , Sebastian Anoo , Mushtaq Muhammad , Al-Shalabi Emad W. , Zeynalli Mursal
Published: April 2024

This study investigates the dynamic adsorption behavior of an ATBS-rich hydrolyzed polyacrylamide (HPAM) polymer onto two different permeability carbonate rocks under varying salinity and high temperature conditions. The approach followed here is aimed at unraveling the complexities of polymer-rock interactions during enhanced oil recovery (EOR) operations and focuses on evaluating dynamic polymer retention in coreflooding experiments performed at 80°C. The analytical techniques used in this research include Ultraviolet-visible (UV-Vis) spectroscopy, Inductively Coupled Plasma (ICP), and Rheometry, which were employed to analyze the behavior of the polymers under these specific conditions. Notably, the results demonstrate that polymer retention levels are significantly influenced by the salinity of the brine and the permeability of the core. In the case of using diluted brine (5,767 ppm) and high permeability core (419 mD), polymer retention levels were substantially lowered, recorded at 37 µg/g-rock. Conversely, in the higher salinity seawater environment (57,670 ppm) combined with a lower permeability core plug (195 mD), retention levels were higher, reaching 57 µg/g-rock. This variation in polymer retention was further supported by the observed low residual resistance factor in conditions where lower polymer retention was recorded. Additionally, the study identified the occurrence of shear thickening behavior for the AMPS-based polymer during in-situ rheology characterization. These findings are crucial for understanding polymer behavior in different reservoir conditions, providing valuable insights for optimizing enhanced oil recovery processes.
Chemical Reduces Risk of Stuck Pipeline Cleaning Equipment in Deepwater Subsea Flowlines
Authors: Rodriquez R. X. , Bufton S. A. , Blanchard T. J. , Schuttler R.
Published: April 2024

An oil export pipeline connected to a deepwater production facility in the Gulf of Mexico requires periodic pigging to manage wax deposition on the pipe wall. The facility used in this paper is in the Viosca Knoll Federal lease blocks, located approximately 120 miles SE of New Orleans, LA in a water depth of approximately 3,250 ft. This facility is a Tension Leg Platform (TLP) floating system set on two pontoons which are anchored to the sea floor. Since it was installed in 1999 many modifications have been made to the facility to allow it to serve as a hub to multiple subsea fields, implementing new technologies encompassing enhanced oil recovery techniques and modified process systems to facilitate safe, effective, and environmentally sound exploitation of the asset base.

The production facility separates oil and gas, then exports each to shore via separate oil and gas export pipelines. It is important to manage wax deposition on the inner wall of the oil export pipeline, which consists of 22 miles of uninsulated 10-in diameter pipe (Fig. 1). This facility's base wax management strategy for the oil export system consists of periodically launching pipeline cleaning pigs and continuously injecting paraffin inhibitor (PI). The current pipeline routine cleaning frequency, pig type, and PI injection rate have been optimized over time.

When the pipeline cleaning pig launcher failed in 2021, a new design needed to be engineered, fabricated, tested, and installed. The expected time required to resume routine pigging for wax management raised concerns regarding excessive wax deposition in the export flowline. Being temporarily without pigging capability triggered mitigation measures such as increased chemical injection concentration, additional scrutiny of pipeline pressure and flow data, and design of a conservative progressive pigging campaign to enable eventual resumption of periodic aggressive maintenance pigging.
The Integrity of Idle and Abandoned Wells in California
Authors: Rehak M. , Warburton D. , Daneshvarnejad N. , Ershaghi I.
Published: April 2024

One of the critical challenges of California's 2045 carbon neutrality plans is the sources of greenhouse gas emissions (GHG) and the states' methods to improve emissions. This work provides insight into plugged wells, idle wells, and their potential for emission of methane and emission interferences these wells have with the states' carbon-neutral plans. Alternative solutions exist to abandon certain idle wells. Repurposing wells can generate revenue for oil and gas operators. By repurposing wells, operators can significantly reduce the cost of decommissioning the wells, highlighting the need for risk assessment in wells with the potential to leak methane.

By studying over 330 randomly selected plugged and abandoned wells throughout California, this study demonstrates how the condition of idle well emission needs further investigation. The potential benefit of confirming if plugged and abandoned wells are leaking methane into the atmosphere is assessed while the methods for characterizing wellbores are identified. Based on California's carbon neutrality goals, assessing the potential of a well for leaking methane into the atmosphere is dependent on the well's integrity changes, improper plugging, and geological shifts, including a detailed approach to the evaluation of characterizing wells based on the potential for cracks in the wellbore cement, inadequate cementation, or likelihood of fluid release through tectonically induced pathways based on wellbore diagrams and the cement class. This study can be of benefit to regulatory agencies and operators for improved development towards GHG emission reduction.
Temperature Warmback Test Design and Interpretation in Presence of Long Injection History
Authors: Zeidouni M. , Kohzadi H. , Zamehrian M.
Published: April 2024

Fluids are often injected at different temperature than the target injection zone in the subsurface. Hence, temperature warmback is observed during well shut-in following injection of cooler fluid. Warmback analysis has been previously presented to determine the injected fluid distribution over the injection zone. However, when the warmback is preceded by a long injection history, warmback analysis can be challenging. The temperature/rate information for the long injection history may not be fully known and if known, it is computationally difficult to account for such large volume of data in the warmback analysis. More importantly, the long injection has likely perturbed the background temperature significantly through various thermophysical processes.

Warmback analysis independent of the injection history should be enabled which is the purpose of this work. We introduce design considerations to conduct the warmback test so that the need for the prior injection history is eliminated. In addition, a simple graphical technique is introduced to enable analyzing the warmback data independent of the geothermal temperature which should be treated as unknown especially in presence of long prior injection. Warmback analysis must become independent of the initial temperature. The graphical method presented in this work requires plotting and line-fitting the observed warmback temperature versus its log-derivative with respect to the shut-in time ratio. The slope of the line-fitted data is inversely proportional to injectivity. Therefore, the graph provides the injection profile when plotted for various flow units (layers) of the laminated injection zone. The graphical method rigor is shown through its application to synthetic and field data.
Integrate Carbon Sequestration in Enhanced Geothermal System Through Surface Dissolution
Authors: Zhang G. , Norbeck J. , Smith A. , Dadi S. , Galban G. E.
Published: April 2024

Enhanced geothermal system (EGS) provides a pathway for geothermal energy development beyond traditional regional constraints, offering a cost-effective and carbon-free energy solution. The potential to integrate carbon capture and sequestration (CCS) within geothermal operations; however, remains an open question to be investigated. This study examines the feasibility of CO2 geological sequestration within enhanced geothermal frameworks. We investigated CO2 storage incorporating a surface dissolution strategy under normal operational conditions of EGS, utilizing thermodynamic models and reservoir simulation. Our findings demonstrate the viability of storing CO2 in geothermal reservoirs, as indicated by the net mass of CO2 storage and the plume migration distance. Hydraulically fractured geothermal reservoirs can accommodate CO2 plume migration, even in ultra-tight reservoirs with permeabilities as low as 0.01 millidarcies (mD). A comprehensive sensitivity analysis, including parameters such as rock porosity, reservoir permeability, thickness, well arrangement, and fracture design, revealed optimal conditions for CO2 sequestration and identified key mechanisms in CO2 storage. Further exploration into CO2 storage in an actual EGS reservoir highlighted the benefits of a complex fracture network, which enhances interactions between wells, fractures, and the matrix. Additionally, we addressed critical issues in implementing CCS with EGS, including sourcing CO2 from direct air capture (DAC) facilities integrated with geothermal power plantsand problems associated with in-situ sequestration. Leveraging geothermal heat, built facilities, and generated energy to operate DAC facilities is a beneficial approach. The advantages are further accentuated when carbon credits are considered. In geological storage, the buildup of injection pressure is a pivotal factor for storage operations within ultralow permeability reservoirs. Another challenge is the exsolution of CO2 from brine during the production process in geothermal operations. The produced CO2, flowing alongside the production well, necessitates separation and cycling back into the system. This research exemplifies the innovative integration of EGS with CCS. By revealing new avenues for CO2 sequestration, we position EGS as a valuable adjunct to conventional CCS techniques. The synergy of technologies underscores an innovative path toward a more sustainable energy landscape.
Mechanisms of Hydrogen Generation During In-Situ Combustion Gasification of Heavy Oil
Authors: Ifticene Mohamed Amine , Yuan Qingwang
Published: April 2024

Carbon-zero hydrogen (H2) production directly from petroleum reservoirs is of paramount importance as the world strives to combat climate change and seeks sustainable energy solutions. In-situ combustion gasification (ISCG) represents a novel approach for leveraging the abundant heavy oil reserves to generate clean hydrogen in situ. However, the crucial reaction mechanisms for hydrogen generation are lack of understanding. It has not been validated which reactions dominate hydrogen generation experimentally during the ISCG process. This study aims to describe the governing chemical reactions and mechanisms of hydrogen generation during ISCG of heavy oils. Experiments were conducted using a self-designed kinetic cell. Heavy oil and its generated coke were used for combustion and gasification experiments, respectively. Results from combustion experiments showed that the heavy oil generated enough coke, and a peak temperature of 600 °C was achieved during combustion. The clay minerals from reservoir sand had a catalytic effect on the oxidation reactions of heavy oils. Gasification experiments showed that hydrogen generation started at 450 °C and peaked at 900 °C. Hydrogen was generated in three different stages. The main mechanism for hydrogen generation was coke gasification followed by the water-gas shift reaction. It was also observed that methane was released due to hydrogen consumption by methanation reactions. Furthermore, the minerals from reservoir sands inhibited hydrogen generation reaction and accelerated hydrogen consumption at high temperatures. Hydrogen generation can be optimized if the reservoir temperature is maintained between 700-750 °C. This work identifies the governing chemical reactions and paves the way for the development of future kinetic models to simulate hydrogen generation through the ISCG process. It highlights the importance of mechanistic control of the process to enhance hydrogen generation and suppress hydrogen consumption reactions. Importantly, it will lay a fundamental foundation for designing the field pilot and improving the success of the implementation of ISCG technology for clean hydrogen production directly from heavy oil reservoirs.
Classification and Evaluation of Microscopic Pore Structure in Carbonate Rocks by Integrating MICP-Based Dynamic Information
Authors: Wang Muyuan , Wu Keliu , Chen Zhangxin , Li Jing , Feng Dong
Published: April 2024

In carbonate reservoirs, the establishment of a coherent correlation between petrophysical static rock type (PSRT) and petrophysical dynamic rock type (PDRT) schemes poses a formidable challenge due to its petrophysical complexity. Additionally, the interpretation of the dynamic properties of microscopic pore structure (MPS) based on mercury injection capillary pressure (MICP) data has been an issue. The objective of this study is to alleviate the divergence in flow properties in MPS classification and evaluate the oil recovery potential of different MPS quantitatively based on MICP.

A total of 76 core plugs without fractures were studied from the Middle East region. The data set available included helium porosity, gas permeability and high-pressure mercury injection. MPSs were qualitatively classified according to the morphological characteristics of the MICP data correlated oil recovery potential. Unsteady-state oil-water relative permeability tests were subsequently conducted to ensure the effectiveness of the classification. Sensitivity parameters were correlated with the efficiency of mercury withdrawal and condensed with the factor analysis (FA) method. After dimensionality reduction, interpretable general factors were obtained to quantitatively characterize the oil recovery potential of MPS and to establish a core quality evaluation model from a dynamic view.

Results showed that the proposed classification can maintain the consistency of dynamic attributes in five qualitative categories and significant differences were observed among the different MPSs. A total of five sensitivity parameters were screened to quantitatively characterize the oil recovery potential of MPS. Moreover, FA defines three aspects that affect the ability to oil recovery: sweep, displacement, and storage. The relative relationship between the MPS and oil recovery potential predicted by the evaluation model and the laboratory-measured oil recovery are in general agreement, and this relative relationship can evaluate the oil recovery potential based on the MPS without the laboratory-measured oil recovery.

This work presents a qualitative classification method for reducing the discrepancy between PSRT and PDRT. The proposed quantitative evaluation model provides new insights into the effects of MPS on fluid flow. Both of them can improve the screening of representative samples for special core analysis and accurate numerical simulation of carbonate reservoirs.
Parametric Study of CO2 Sequestration in Deep Saline Aquifers Using Data-Driven Models
Authors: Khan M. I. , Khanal A.
Published: April 2024

Large-scale geo-sequestration of anthropogenic carbon dioxide (CO2) is one of the most promising methods to mitigate the effects of climate change without significant stress on the current energy infrastructure. However, the successful implementation of CO2 sequestration projects in suitable geological formations, such as deep saline aquifers and depleted hydrocarbon reservoirs, is contingent upon the optimal selection of decision parameters constrained by several key uncertainty parameters. This study performs an in-depth parametric analysis of different CO2 injection scenarios (water-alternating gas, continuous, intermittent) for aquifers with varying petrophysical properties. The petrophysical properties evaluated in this study include aquifer permeability, porosity, relative permeability, critical gas saturation, and others. Based on the extensive data collected from the literature, we generated a large set of simulated data for different operating conditions and geological settings, which is used to formulate a proxy model using different machine learning methods. The injection is run for 25 years with 275 years of post-injection monitoring. The results demonstrated the effectiveness of the machine learning models in predicting the CO2 trapping mechanism with a negligible prediction error while ensuring a low computational time. Each model demonstrated acceptable accuracy (R2 >0.93), with the XGBoost model showing the best accuracy with an R2 value of 0.999, 0.995, and 0.985 for predicting the dissolved, trapped, and mobile phase CO2. Finally, a feature importance analysis is conducted to understand the effect of different petrophysical properties on CO2 trapping mechanisms. The WAG process exhibited a higher CO2 dissolution than the continuous or intermittent CO2 injection process. The porosity and permeability are the most influential features for predicting the fate of the injected CO2. The results from this study show that the data-driven proxy models can be used as a computationally efficient alternative to optimize CO2 sequestration operations in deep saline aquifers effectively.
Numerical Simulation of CO2 Storage in the Soft Sediments of Depleted Reservoirs in the Gulf of Mexico
Authors: Hashemi Sam S , Kovscek Anthony
Published: April 2024

Depleted oil and gas reservoirs are among the most popular formations for large-scale geologic CO2 storage. While extensive research and characterization have been conducted on the geological structure and physical properties of many of these fields, the lack of field and core sample data related to reservoir flow and mechanical properties has hindered the assessment and modeling of CO2 storage, especially in offshore areas. In this study, we developed a coupled flow-geomechanical numerical model to evaluate the potential for storing CO2 in the soft sediments of the West Delta field of the Gulf of Mexico. The poroelastic parameters of the reservoir rocks were measured in the laboratory and the geological model of the reservoir was constructed based on the available seismic and well logs. The process of reservoir depletion was simulated to consider the effect of porosity reduction and permeability change. Pore volume multipliers were employed at the boundary to reach realistic pressure in the reservoir after depletion. A modified Cam-Clay model was used as the mechanical failure criterion to trace the porosity and formation subsidence. The results showed that the porosity reduced somewhat during the depletion and then increased during the CO2 injection. The porosity, however, did not fully recover after the injection process was completed. The maximum subsidence of the storage formation top reached almost 24 cm at the well location once the depletion completed. The pressure buildup at fault zones varied based on the vicinity of faults to the injection well, fault dip, dip direction, and CO2 injection rate. Sensitivity analysis using Fault Slip Potential (FSP) showed that various geomechanical parameters such as SHmax orientation, friction coefficient and dip of the fault influenced the probability of fault slip markedly. Assuming that faults are sealed, the pressure perturbation required to slip one of the major faults in the reservoir volume studied was lower than the calculated pressure buildup after the CO2 injection. The other two major faults are not predicted to have the potential for activation in any scenario. Therefore, it is advisable to conduct additional mechanical characterization particularly focusing on in-situ stress orientation and the transmissibility of faults as further work. This precautionary measure aims to mitigate the risk of fault activation during or after the injection phase.
Experimental Investigations of Caprock Integrity for Geological Carbon Storage Using Three-Dimensional Digital Image Correlation
Authors: Nath F. , Cabezudo E. , Romero N. G.
Published: April 2024

Caprock integrity is a critical concern in the successful implementation of geologic carbon capture and storage (CCS) projects. Caprock can break mechanically by radial cracking, plastic deformation, inner and outer debonding, and channelling. Understanding these failure mechanisms is crucial for designing robust management strategies in CCS applications. The classical approach is limited, so this study uses three-dimensional digital image correlation (3D-DIC) to study caprock sample deformation and failure for carbon capture and storage (CCS) reservoir to ensure caprock integrity.

Caprock samples from CCS reservoirs were put through diametrical compression using a precise 100 kN electro-mechanical load frame that moved at a rate of 0.05mm/min. The samples had a wide range of minerals. During the diametrical compression tests, a 3D-DIC image capture system was set up to watch the samples without touching them at a rate of 5 frames per second. A black-and-white speckle pattern is affixed to the specimen to monitor its deformation underload. The 3D-DIC system is used for image processing, visualization, and analysis of the caprock sample damage process under various load circumstances.

The study uses DIC-generated strain maps to analyze the process of caprock samples breaking, revealing a tension-compression ratio between 2% and 5%. The damage evolution process is divided into four stages: initial, linear elastic, elastic-plastic, and plastic damage. The results show that shale damage changes over time when samples have anisotropy and distinct heterogeneity, potentially causing caprock integrity issues.

This study focuses on predicting damage processes in caprock integrity in CCS using various methods. 3D-DIC outperforms these methods in terms of test range, affordability, accuracy, and field monitoring. This image-based algorithm is better at understanding anisotropic and heterogeneous conditions. The results will enhance the effectiveness of examining caprock integrity in CCS outperforms other methods in terms of test range, affordability, accuracy, and field monitoring.
Numerical Investigation of Hydrogen Storage Loss in Saline Aquifers
Authors: Khanal A. , Khan M. I. , Shahriar M. F.
Published: April 2024

Underground hydrogen storage (UHS), a large-scale and long-term energy storage system, can augment decarbonization prospects and drive the renewable energy sector forward due to the attractive energy capacity and environmentally friendly features of hydrogen (H2). One of the primary concerns of UHS is the in-situ loss of the injected H2 via different rock-fluid interactions. This study uses numerical simulation models to investigate the loss of H2 associated with the effects of different rock-fluid interactions and mass transfer mechanisms. Sensitivity studies were conducted to understand the impact of various factors, including the relative permeability hysteresis, capillary pressure, dissolution, and diffusion of H2 in brine. Furthermore, we also evaluated the effect of uncertainty in capillary pressure curves on trapped H2 using the parameters generated from an extensive literature survey. Finally, we compared the reservoir behavior in a finite and infinite-acting reservoir. The results showed that almost 20% of the injected H2 gets trapped in the reservoir due to the impact of the relative permeability at variable brine saturations. Furthermore, the results demonstrated that the relative permeability hysteresis during the imbibition phase significantly reduces the recoverable H2 due to residual trapping. Additionally, capillary pressure and dissolution moderately impacted the amount of trapped H2 during UHS. However, diffusion showed negligible impact on the final amount of H2 produced from the reservoir. The H2 loss in an infinite reservoir was significantly higher compared to the finite reservoir. To summarize, by analyzing the different H2-Rock-Fluid interactions, this study provides novel insights into the flow behavior of H2 in subsurface porous media.
Technical Assessment of Temporary Gas Storage to Avoid Flaring: A Case Study
Authors: Aoun Ala Eddine , Tomomewo Olusegun , Ameur Mohamed Cherif Ben , Masadeh Mohammad , Khetib Youcef
Published: April 2024

Environmental performance is a significant driver of energy production today, and providing attractive methods to reduce flaring remains a high priority for oil producers. Oil production from the Bakken and Three Forks formations has significantly increased over the last ten years without commensurate augmentation of gas capture infrastructure. This has resulted in the gas gathering network and treatment facilities operating at maximum capacity, leaving the system vulnerable during maintenance or operational emergencies. This study investigates the potential solution to mitigate flaring in case of temporary gas plant shutdown. The project’s main objective is to evaluate technically the feasibility of gas re-injection in the Bakken formation as a means to temporary store gas, increase oil recovery, reduce flaring and maintain compliance.

A flow simulation model was created and history matched to investigate continuous gas injection of a four-well pad. Different gas injection schemes were examined to identify the best scenario for temporary gas storage, assess the incremental production, analyze the effect on offset wells, evaluate gas recovery and efficiency. Gas reinjection has the added benefit of producing incremental oil that can offset installation and operational costs associated with gas compression and nonproducing time for a well during injection.

Results indicate that gas injection rate of 2.8 MMcfd has the highest gas injection efficiency, up to 0.3 bbl of oil equivalent per Mcf of gas injected. In addition, the increase in the gas production rate during injection phase is manageable, less than 0.7MMcfd. High gas injection rates yielded better gas recovery up to 80%, however lower gas injection efficiency and early gas breakthrough are expected. Low gas injection rates could produce incremental oil with moderate impact on offset wells. Communication of gas is likely to occur between wells within 1500 feet and wells positioned heel to heel that are common to the well-pad facility. The most promising injection scheme appeared to be dedicated injection of 2.8 MMcfd.

The findings support continued work to further investigate well configurations that can best benefit from the technique and to mature economic case studies.

This study is an important initiative aimed at addressing environmental concerns related to oil production in the Bakken and Three Forks formations. Gas re-injection is being explored as a viable solution to reduce flaring, increase oil recovery, and ensure compliance with environmental regulations. It represents a proactive approach by the oil industry to mitigate its environmental impact and optimize resource utilization.