bluuebunny/crossref_metadata_2025_split huggingface dataset:
Binary Gas Transport with Multilayer Adsorption in Nanoporous Media
Authors: Babatunde Kawthar A. , Emami-Meybodi Hamid 
Published: September 2024

Abstract
We investigate binary gas transport with multilayer adsorption in nanoporous media by merging pore-scale properties obtained via multicomponent simplified density theory (MSLD) with continuum-scale transport modeling. Accordingly, we propose a diffusion-based gas transport model that considers the free and sorbed phases and multilayer multicomponent adsorption in the nanoporous media and incorporates a dynamic sorbed-phase density. Bulk, Knudsen, and viscous diffusion are considered in the free phase, while surface diffusion and multilayer adsorption are considered in the sorbed phase. The proposed model is utilized to investigate the impact of fluid-solid interactions and pore size on gas transport and storage using different fluid-solid systems. It was also used to perform gas transport simulation and a comparison with monolayer adsorption model was made. The results show that the pressure and composition diffusion coefficients are smaller in strong adsorption affinity systems due to increased adsorption and reduced free-phase volume fraction. The pressure and the composition capacity factors are higher for stronger adsorption affinity systems. The results also reveal that the sorbed phase occupies a significant fraction of the pore space and can occupy the entire pore volume for sufficiently small nanopores, depending on pressure and fluid-solid interactions. Accordingly, the mass transport is dictated by the sorbed phase, with its influence increasing as the fluid-solid interaction increases. Furthermore, the free phase contribution to mass transport increases with pore size. On comparing the mass flux obtained using the proposed model with the mass flux obtained using a monolayer adsorption model, a slightly lower contribution of the sorbed phase to total mass flux was observed for the multilayer model. The proposed model incorporates pertinent multicomponent gas transport and storage mechanisms in nanoporous media on a continuum scale but informed by pore-scale phenomena.
GCBD Implementation: Strike a Balance Between Dynamic in Gas Supply and Demand and Late Life Field Investment
Authors: Izza N. B. , Idrus S. , Harun N. , Mohammad S. Z. , Usop M. , Teoh Y. W. 
Published: September 2024

The gas portfolio strategy in an oil field becomes significant as reservoirs or fault blocks approach depletion. An assessment of gas supply and demand reveals a potential shortfall in Peninsular and East Malaysia before 2026. This situation creates an opportunity to capitalize on associated gas and non-associated gas (NAG) to fulfill the demand, leveraging existing facilities that remain within their operational life. This will also enhance field recovery in late life and depleting fields before decommissioning. Gas Cap Blowdown (GCBD) involves a process of depressurizing the gas cap of an oil reservoir to produce the associated gas cap (AG) volumes, more often after the oil rim has been depleted of significant oil production. Notably, in Northeast Malaysia, the gas cap project has yet to be undertaken, as the predominant attention of future resource growth focuses on deepwater development. This underscores the strategic importance of optimizing gas resources and exploring new initiatives to address evolving energy demands in the region.
Gas portfolio development and master plan is currently a high priority effort by host government since several oil fields, reservoirs or fault blocks are nearing depletion. Therefore, it is imperative to have a standardized approach for developing the Gas Cap reservoirs. This paper presents an insightful strategy for late field life management, focusing on key triggers for GCBD and how the project team secure appropriate approvals from the host government regulatory body for the development of gas cap resources in response to gas demand.
From the lenses of host government, several key considerations or triggers should be in the primary evaluation in deciding optimum timing for partial or full blowdown as shown in Figure 1. Where gas development may impact existing or potential future oil development, key decision/triggers points are embedded in the workflow whether to further study, appraise, or accept that there is no scope for further oil developments and GCBD can be executed. Among the criteria are oil depletion, molecule tradeoffs, facilities age and its design limit, economic life and ultimately demand availability. Detailed study is required to determine the oil vs gas trade off evaluation on the timing of blowdown opportunities.
Revolutionizing Drilling Operations: Next-gen Llm-AI for Real-time Support in Well Construction Control Rooms
Authors: Ferrigno E. , Rodriguez M. , Davidsson E. 
Published: September 2024

Abstract
In the fast-paced and precision-driven environment of well construction operations, efficient decision-making is paramount. This paper introduces an advanced artificial intelligence (AI) model leveraging Large Language Models (LLM) to classify log records and complex WITSML data in real time. By integrating this AI model into drilling control rooms, decision-making efficiency has been enhanced by over 50 times, significantly reducing the time required for search and background analysis from hours to minutes.
The implementation of LLMs such as GPT-3 and GPT-4 has revolutionized the process by enabling zero-shot inference, allowing for the accurate classification of complex texts without extensive labeled training datasets. This breakthrough has facilitated the rapid processing and analysis of structured and unstructured data, which is crucial for real-time response to critical events in drilling operations.
A collaborative effort between drilling experts and digital developers resulted in a functional proof of concept within two weeks, expanding to a minimum viable product (MVP) in under 90 days. The AI model now categorizes technical texts into 42 distinct categories, surpassing initial goals and demonstrating substantial improvements in both accuracy and processing speed.
This paper concludes by highlighting the significant operational efficiencies and data consistency achieved through the AI model. It also discusses potential future enhancements, including the integration of diversified data sources and the development of predictive and prescriptive models, positioning this AI-driven approach as a cornerstone for future advancements in well construction control rooms.
Effect of Injection Rate on the Heat Exchange Process in EGS: Insights from Laboratory Experiments
Authors: Zhao Peng , Zhu Haiyan , Chen Shijie , Zhang Zhaopeng 
Published: September 2024

Abstract
The flow and heat transfer characteristics in the hot dry rock (HDR) fracture system are crucial for the efficient development of geothermal energy. To explore the influence of fluid leakage and the injection-production process on the long-term cycle of geothermal reservoirs during the heat exchange process, this work introduces the development of an in-situ system for HDR geothermal resources and conducts a large-scale physical simulation experiment of long-term injection-production flow and heat exchange using this system. This provides a simulation method for developing HDR geothermal resources. The study systematically analyzed fluid leakage, production pressure difference, production flow rate, production well exploitation temperature, and monitoring well temperature changes during low-temperature fluid injection. Additionally, it deeply explored the fluid recovery rate, the influence of heat exchange medium leakage on heat transfer performance, and the timing of these actions. The results indicate differences in fracture conductivity among various heat exchange channels during the process, leading to different factors affecting the heat exchange rate at different stages. During heat transfer, the exploitation temperature of the production well shows a downward trend, and higher flow rates result in faster declines. In the middle and late stages of injection-production heat transfer, the leakage channel activates under the combined effects of high osmotic pressure. The conductivity of the leakage channel gradually increases during the heat exchange process, severely affecting the long-term exploitation of geothermal resources.
Chemical and Phase Equilibrium Calculations Applied to Dissolution and Precipitation of Multiple Solid Phases
Authors: Oyenowo Oluwafemi P. , Luo Bo , Okuno Ryosuke 
Published: September 2024

Abstract
Mineralization stands out as the most reliable method for carbon dioxide (CO2) storage, transforming CO2 into stable carbonate minerals that remain locked away from the atmosphere. This high security has sparked interest in enhancing CO2 mineralization within geological formations. However, effective enhancement demands a robust thermodynamic model that integrates chemical and phase equilibria (CPE).
In this paper, we introduce a CPE model built upon the Pitzer activity and Peng-Robinson fugacity models, solving it through Gibbs free energy minimization. The CPE model can model complex systems involving multiple hydrocarbon phases, an aqueous phase, and several solid phases, marking a significant advance in fluid modeling for carbon capture, utilization, and storage (CCUS) technologies.
We applied the CPE model to analyze experimental data, particularly the enhanced dissolution of basalt minerals using sodium formate solutions. Our case studies highlight the model's versatility. One case demonstrated the model's capability to represent seven distinct phases, including an oleic, a gaseous, an aqueous, and solid phases, during CO2 injection into a depleted oil reservoir. In another novel application, the CPE model analyzed ligand-promoted basalt dissolution, revealing that metal-formate compounds in the aqueous solution lowered the chemical potentials of dissolved solids, thereby enhancing mineral dissolution. These findings confirm the CPE model's potential to drive forward CO2 mineralization strategies.
Geomechanical Modeling and Drilling Challenges in Deepwater Salt Thrust Belt, Case Histories from GOM
Authors: Shaker S. S. 
Published: September 2024

Abstract
Drilling wildcats along the overthrust salt belt exploration trend in the Gulf of Mexico (GOM) is a challenging mission. At the early stages of exploring for oil and gas in this profitable salt thrust belt during the 1990's, drilling to the objective prospective traps suffered multiple hurdles to accommodate for the creeping salt mass. Excessive loss of circulations at the exit boundary between the salt and the subsalt sediments was the dominant challenging operation. Spotting lost circulation material associated with reducing the mud weight was not enough to recover the damaged bore-hole. Sidetracks and bypasses that increases the drilling cost became the norm. Maneuvering the mud pressure within the open bore-hole between the pore (PP) and fracture (FP) pressures within the deepwater narrow drilling window need to be systematically done based on the adequate geomechanical modeling for this unique geological setting. The presence of mobile creeping salt marching toward the Abyss modifies and alter the conventional geomechanical – pore and fracture pressure coupling where vertical overburden sediment load (OB) acting as the principal stress (S1). Therefore, applying the conventional effective stress formation pressure prediction methods, where the principal stress is the vertical load OB can lead to serious mishaps. The lack of cohesive seismic velocity due to the salt perturbation added uncertainty to any predrilling velocity – pressure prediction transformation modeling. Reviewing the geological evolution of the salt – sediments interaction and the physical properties of each major units since the salt advancement up till reaching the final destination in the Abyss at the Sigsbee Escarpment is the back-bone of this new endeavor to predict PP-FP window. In addition, all the petrophysical logging data, pressure measurements and drilling events were tabulated and correlated to the unique geological development in this area. Depth dependent numerical equations were established as a PP-FP prediction method.
Enhanced 3D Pore Segmentation and Multi-Model Pore-Scale Simulation by Deep Learning
Authors: Li Haotian , Aslam Billal , Yan Bicheng 
Published: September 2024

Abstract
Rock permeability characterization is crucial to understanding fluid flow in subsurface geological formations. It contributes to accurately simulating such processes that can address challenges like sustainable hydrocarbon production and geological CO2 sequestration. Recent advancements in deep learning have facilitated efficient permeability prediction in digital rock. However, existing methods often struggle to predict core-scale properties due to limitations in accommodating larger sub-volumes. This study introduces novel approaches integrating deep learning and physics-constrained methods to enhance rock segmentation, permeability prediction and upscaling. We first propose a 3D Inception U-Net model for 3D pore segmentation, which leverages the capability of the Inception block to capture multi-scale features in porous media and thus enhances segmentation accuracy. Further, we develop two different upscaling methods for permeability prediction. The first method is direct upscaling using deep learning, which directly predicts permeability across multiple scales by training with a combination of various sizes of sub-volumes; the second method is physics-constrained upscaling using deep learning, which imposes additional physical constraints on permeability predictions.
We evaluate our deep-learning-based segmentation and upscaling approaches on diverse datasets, including Bentheimer, Leopard, and Parker sandstones. Our 3D Inception U-Net model achieves 0.99 accuracy for 3D pore segmentation. In upscaling, the direct upscaling using deep learning achieves R2 scores of 0.94, 0.83, and 0.84 at sub-volume sizes of 1503, 3003, and 6003, respectively, which demonstrates its potential to generalize permeability prediction across multiple sub-volume scales. On the other hand, with the permeability prediction of the base sub-volumes (size 1503) through the Lattice-Boltzman Method (LBM), the physics-constrained upscaling using deep learning achieves R2 values of 0.98 after upscaling from 1503 to 3003 sub-volumes, and further increases R2 to 0.99 after upscaling from 3003 to 6003 sub-volumes. Furthermore, when using 3D CNN-predicted permeability of 1503 sub-volumes, the second upscaling method achieves R2 scores of 0.96 and 0.94 for these two upscaling stages, respectively, highlighting its stable accuracy across scales. This research highlights the potential of integrating advanced deep learning with physics-constrained approaches to advance rapid and precise permeability prediction in digital rock physics, offering a promising framework for future core-scale applications and research endeavors.
Would Self-Supported Fracture Contribute to the Hydrocarbon Production in Shale Reservoir Besides Proppant-Supported Fracture
Authors: Sun Y. , Li G. , Zeng S. , Wu J. , Liu J. , Xu M. , Dai C. , Bai B. 
Published: September 2024

Abstract
During hydraulic fracturing in deep shale gas reservoirs, it is difficult to pump proppants long distances, and self-supported fractures are formed at the far and upper ends of the fractures. The self-supported fracture could hold the fracture space by its surface structure again fracture closure. However, it faces various aspects of impairment during shale gas reservoir development, which affect its conductivity. Among the impairment, long-term production results in a decline of bottom hole pressure, which will compact the fracture space, and self-supported fractures without proppant are more sensitive than those with proppant.
In this paper, we conducted a series of experiments with samples from a deep shale gas reservoir in the Sichuan basin of China. The self-supported fracture conductivity behavior at high temperatures and high-pressure conditions was studied from the aspects of various fracture dislocation distances, pressure reduction methods, and well-open and shut-in times. Then, the mechanism of self-supported fracture impairment was revealed from the change in fracture width, fracture surface roughness, and fracture surface microscopic morphology. With the ideal numerical model, the impact of self-supported fracture ratio, and pressure drop methods on the gas production were analyzed.
The results indicate that the dislocation of self-supported fractures can improve its conductivity greatly. The self-supported fracture conductivity with 1 mm dislocation was increased by 18.5 times to 76.1 times compared with those without dislocation. With the production continuing, when the net pressure on the self-supported fractures is above 35 MPa, its conductivity was impaired significantly, reaching 51.2-92.0%. Small pressure reduction step/more frequent nozzle replacement and reducing the well open and shut-in times would help protect the self-supported fracture conductivity. When the net pressure on self-supported fractures increased from 10 MPa to 30 MPa, the equivalent width of fractures decreased by 53.3%. The average height of the fracture surface decreased by 30.7% after pressure compaction. The numerical modeling study indicates that the larger the volume of self-supported fractures, the higher the productivity of the gas well. In a 3-year production time, when the ratio of proppant-supported fractures to self-supported fractures increased from 1:0.25 to 1:5, the cumulative gas production increased by 220.8% (90.7×106). The small-pressure drop production has higher cumulative productivity than the high-pressure drop method. The cumulative gas production is 115.2×106 when a 10 MPa pressure drop is used, while it is 80.9 ×106 with a 50 MPa pressure drop. This study could provide a theoretical basis for hydraulic fracturing and production system optimization of deep shale gas reservoirs.
Deep Learning Assisted Multi-Objective Optimization of Geological CO2 Storage Under Geomechanical Risks
Authors: Zheng F. , Ma Z. , Viswanathan H. , Pawar R. , Jha B. , Chen B. 
Published: September 2024

Abstract
In geological CO2 storage, designing the optimal well control strategy for CO2 injection to maximize CO2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO2 plume migration, the highly nonlinear nature of geomechanical responses during CO2 injection on rock-fluid interaction, as well as the high computational cost associated with coupled flow-geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO2 storage while minimizing geomechanical risks by adjusting the injection well's bottom-hole pressure (BHP) within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by the seismic moment. We employ the Fourier Neural Operator (FNO) based deep learning model to construct surrogate models, replacing the time-consuming coupled flow-geomechanics simulations for evaluating the objective function. The input features of the FNO-based proxy model comprise flow properties and well controls. Meanwhile, the outputs consist of time-series reservoir pressure and CO2 saturation as the state variables. The FNO-based proxy model is trained on a synthetic case simulating large-scale CO2 storage. The results demonstrate that the deep learning-based proxy models accurately and efficiently predict the designated state variables. The developed proxy models have been incorporated into a multi-objective optimization framework through a Genetic Algorithm (NSGA-II) to reduce the computational burden. The proposed optimization framework exhibits significant computational acceleration compared to conventional simulation-based function evaluation strategies. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, diverging from scenarios where geomechanical risks associated with CO2 injection are disregarded. Our findings reveal that the injection rates may not need to reach their upper bounds to achieve the highest storage capacity when geomechanical risks are considered. This is significant because pushing injection rates too high can induce rock failure and unacceptable levels of micro-seismic and leakage events. Thus, the maximum storage capacity is constrained to maintain the reservoir in an elastic state. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as an objective function into the CO2 storage optimization workflow to mitigate the potential risk of induced micro-seismicity and CO2 leakage. Second, leveraging the FNO-based proxy model to optimize a real CO2 storage operation represents a new approach.
Utilization of Carbon Dioxide for In-Situ Precipitation of Calcium Carbonate for Conformance Control in Heterogeneous Porous Media
Authors: Willoughby Christian E. , Balhoff Matthew T. , DiCarlo David A. 
Published: September 2024

Abstract
Conformance control is a necessary part of enhanced oil recovery operations in heterogeneous oilfields. However, many techniques center on near-wellbore conformance or are not well suited for low matrix permeability formations. We propose the injection of calcium hydroxide and carbon dioxide as a novel conformance control strategy in reservoirs that experience poor sweep efficiency due to heterogeneity. These two chemical agents react to precipitate solid calcium carbonate, potentially blocking already-swept pores and directing injected fluids to unswept regions of the reservoir. Microfluidic devices (micromodels) were used to perform experiments to observe the fundamental behavior of this reaction in porous media. Precipitation grain size, location, and relative quantity were directly observed while changing injection order, CO2 state, and injection rate. It was found that the reaction took place in porous media for both states of CO2 (gaseous and dissolved in deionized water). Injecting at a lower rate (0.33 ft/day) resulted in less precipitate, but larger grains during single-phase (aqueous) experiments than injecting at a higher rate (1 ft/day). Additionally, more precipitate formed during experiments in which gaseous CO2 was used, compared to when CO2 was dissolved in water. Overall, precipitation occurred where the reactants both contacted each other and mixed, which did not have a preference for high or low permeability zones. Precipitation was also shown to block pore throats in a 2.5D micromodel, reducing the permeability by 92.6% at a 10 ft/day interstitial injection rate. Precipitation via this method can be controlled through CO2 placement in the reservoir and injection rate, as the reaction only occurs where CO2 and Ca(OH)2 are both present and have sufficient convective mixing.