bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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

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.
A New Simplified Kinetic Model for Hydrogen Generation During In-Situ Combustion Gasification of Heavy Oil
Authors: Ifticene Mohamed Amine , Li Yunan , Song Ping , Yuan Qingwang
Published: September 2024

In the global push for sustainable energy, in-situ combustion gasification (ISCG) has emerged as a transformative technology to leverage the world's abundant heavy oil reserves for producing carbon-zero hydrogen. Chemical kinetics are crucial for modeling subsurface hydrogen generation and optimizing production schemes to maximize hydrogen yield, which are however currently lacking. This study aims to develop the first experimentally validated kinetic model for hydrogen generation during ISCG of heavy oil. To accurately model ISCG reactions, particularly hydrogen generation, we combined kinetic cell experiments with numerical modeling to history-match the experimental results. The temporal variation of generated gases, such as hydrogen, measured in laboratory experiments, served as the baseline for history matching. A differential evolution optimization algorithm was employed to calibrate the kinetic parameters of the numerical model with experimental results. The kinetic model for combustion reactions was accurately calibrated using combustion experiments. This accuracy is attributed to the well-studied nature of heavy oil oxidation and the comprehensive reaction scheme employed. Conversely, calibrating the kinetic model for gasification reactions with kinetic cell experimental results proved more challenging. Despite significant uncertainties in hydrogen generation and consumption reactions due to limited knowledge of the gasification process, our proposed kinetic model can still predict hydrogen generation with a simplified but powerful reaction scheme, compared to previously proposed ISCG models that involve numerous reactions. This work introduces the first kinetic model to describe the hydrogen generation process during ISCG of heavy oil with rigorous experimental validation. This reliable kinetic model establishes a solid foundation for future multi-scale reservoir simulation and further optimization of the field development for enhanced hydrogen production in a more sustainable manner.
Assessment of Storage Efficiency Factor of Deep Saline Aquifers Using Early 500 Ktonnes of Injected CO2 at the Aquistore CCS Site
Authors: Vahidinia Shima , Shokri Alireza Rangriz , Chalaturnyk Rick
Published: September 2024

Estimation of effective storage capacity of the subsurface formations is currently one of the main technical and financial challenges for the Carbon Capture and Storage (CCS) industry. This factor indicates that not all available pore space will be accessible for CO2 storage. Even within accessible pore spaces, complete saturation with CO2 might not occur. This paper presents the evolution of storage efficiency factor during CO2 injection into a hyper-saline clastic formation.

Values of storage efficiency factor were estimated based on 8 years of CO2 injection at Aquistore. Aquistore, the storage component of the Boundary Dam CCS project in Canada, aims to address the practical aspects of injecting large scale CO2 into a 3.4 km deep aquifer. The process involved analyzing CO2 injection data and employing multiple realizations of reservoir simulations that were calibrated to the time-lapse seismic surveys. We used the USDOE static storage estimation methodology for each geological layer in addition to the whole saline system and compared the results with dynamic CO2 storage efficiency from Aquistore.

Multiple detailed geological and property models were derived from well logs and core analysis at Aquistore. Reservoir simulation cases were constructed for both the storage formations and the overlying caprock to explore the impacts of geology, trapping mechanisms, and injection schemes on storage efficiency. A wide range of time-dependent storage efficiency values were determined for each geological layer through history matching the flow simulation models to the Aquistore injection history in addition to the CO2 plume propagation determined from multiple 4D seismic data. The impacts of assumed degree of uncertainty in geologic and flow properties were considered on the overall storage efficiency factor. The estimation was highly affected by cutoff parameters to delineate the net thickness of the storage formations as well as the averaging technique used for petrophysical properties, especially in the case of multi-perforated zones. Lastly, the evolution of effective storage efficiency was discussed in the context of cold CO2 injection where non-isothermal localized fracturing, salt precipitation, and multiphase conditions could affect the early CO2 injectivity.

Utilizing actual field data of over 500 ktonnes CO2 injection from the Aquistore CCS project is an asset to storage efficiency and capacity estimations. Our results provide a better understanding of evolution of storage efficiency factor in saline aquifers, offering valuable insights into the long-term prospects of geological CO2 storage projects.
Field-Scale Optimization of CO2 Injection Schedule with Critical Pressure and Plume Boundary
Authors: Fu Jia , Alghawi Yaser , Datta-Gupta Akhil , Manche Cameron , Clark Jim
Published: September 2024

This study aims to develop a field-scale CO2 injection optimization workflow to refine the injection strategy and enhance storage capacity while managing the extent of the Area of Review (AoR). We utilize the concepts of critical pressure and plume boundary to evaluate the AoR delineation during injection optimization. The optimization workflow is enhanced by an advanced upgridding technology to accelerate simulations. We evaluate the feasibility of CO2 storage with AoR delineations using varied injection schedules.

We employ an advanced upgridding technique to accelerate the optimization workflow. Our approach involves an ‘optimal’ layer design through sequential coarsening using a distance-based error metric while minimizing the loss of heterogeneity between fine-scale and coarsened models. A recursive analysis is applied to develop the optimal layering while preserving geological heterogeneity and reducing the number of layers for computational efficiency. After optimal layer design, a novel pressure transient upscaling is introduced to upscale transmissibility based on local flow solutions, which preserves reservoir quality and reservoir flow barrier and is particularly well-suited for high-contrast systems.

Our proposed workflow is tested to optimize the injection schedule for field-scale application of CO2 storage in saline sands along the Texas Gulf Coast. Field-scale evaluation and optimization of CO2 storage using a high-resolution compositional model can be computationally demanding. This is compounded by the fact that optimization of the injection schedule requires a large number of flow simulations to examine a multitude of scenarios. First, the performance of the upgridding scheme was evaluated based on computational speed up and comparison of the well responses and dynamic reservoir properties from the fine-scale model. We examined three different approaches for distance-based errors to quantify the loss of heterogeneity: velocity-based distance, slowness-based distance, and hyper-volume weighted distance. The optimal layer design scheme resulted in approximately an order of magnitude speed up in computation time, which greatly facilitated the field-scale CO2 injection optimization. The injection schedule was optimized to maximize storage capacity while minimizing the AoR extent to avoid potential contamination of USDW. The injection zone selection involved the evaluations of flow unit geometry, reservoir properties, zone interference, and their influence on the AoR extent. The optimization of the injection schedule was carried out iteratively and heuristically, applying learnings from previous simulation scenarios.

The proposed workflow provides an efficient way to evaluate AoR extent and optimize the injection schedule while providing valuable guidelines for future carbon storage projects.
Downhole Pressure Wireless Monitoring via Electromagnetic Waves Transmission in Oil Well Production
Authors: Yang Haitao , Lu Hongjun , Li Dajian , Wang Yahui , Zheng Gang , Gan Qingming , Rong Guanghui , Hu Zijian
Published: September 2024

Pressure monitoring is one of the crucial dynamic parameters in the production process of oil wells. Due to the large number and wide distribution of oil wells, the use of wired monitoring technology aims to reveal downhole production dynamics in some high-production-rate or key wells, there are difficulties such as high production costs, large analysis workload, high risks, and maintenance of monitoring tools, which are not suitable for large-scale promotion. The lack of intuitiveness and timeliness in conventional storage pressure monitoring has brought enormous difficulties to the normal management of oil wells. However, the wireless data transmission method of Electromagnetic Waves Measurement While Drilling (EM-MWD) cannot be directly implemented into oil production wells.

On the basis of technical research and drawing on the wireless monitoring experience of EM-MWD, the 3 different electromagnetic wave excitation methods were compared, and an asymmetric oil tube antenna electromagnetic wave excitation method suitable for matching the characteristics of the pumping well was identified. Mathematical models of the wellbore formation were established. Based on finite element analysis, a simulation model of electromagnetic wireless monitoring under the pumping well was constructed, and the influencing factors of electromagnetic wave transmission were analyzed, including the influence of formation wellbore, casing, and well fluid media on electromagnetic waves. After conducting research on the structural principles and weak signal processing methods, a wireless monitoring system for ultra-low frequency electromagnetic wave downhole monitoring data, combined with downhole electromagnetic wave transmitter and surface receiver for pumping wells, is formed.

By installing a transformer on the output end and adjusting the voltage, the simulated test ground can achieve electromagnetic wave signal acquisition at a depth of 1500 meters and meet the analysis requirements. Through on-site testing at X well, when the well depth reaches 1517m, the signal can be transmitted normally; The signal acquisition frequency band of the wireless transmission system for underground pressure is once per 5-1440 minutes, and the pressure measurement range is 0-100MPa; The accuracy of downhole measured pressure data by sensors reaches 80%.

The development of a wireless monitoring system for downhole pressure in pumping wells is of great significance for dynamic monitoring of reservoir pressure and intelligent reservoir construction. Downhole dynamic monitoring achieves intuitive and timely monitoring, providing a new tool for real-time understanding and mastery of reservoir dynamic changes; Compared to conventional cable control monitoring methods, it is expected to significantly reduce costs and help promote on-site application.
East Nesson Bakken Huff ‘n’ Puff Enhanced Oil Recovery Second Pilot: Water-Alternating-Gas Injection using Produced Gas and Water
Authors: Pospisil G. , Griffin L. , Pearson C. M. , Dalkhaa C. , Sorensen J. , Kurz B. , Bosshart N. , Assady A. , Zhao J. , Warmack M. , Ren G. , Giesbrecht R. , Herman G. C.
Published: September 2024

In 2023, Liberty Resources LLC (Liberty) deployed an enhanced oil recovery (EOR) pilot via a single huff ‘n’ puff (HnP) well in a 2560-acre spacing unit at the East Nesson site in Mountrail County, North Dakota. This was the second EOR pilot at this site and the third by this operator in the Bakken. The prior pilot at the East Nesson site was deemed effective, resulting in incremental oil production by using a novel technology for coinjecting pulses of produced gas and freshwater with surfactant. The objective of this second pilot was to improve economics and simplify injection operations by using readily available equipment and sources of water and gas in a water alternating-gas (WAG) scheme. Oil recovery mechanisms include raising reservoir pressure above the minimum miscibility pressure and improving gas conformance to displace incremental oil in a scalable process.

Numerical models were developed and calibrated using historical production data and learnings from the prior EOR pilots. Various injection scenarios were simulated including the use of surfactants and alternative WAG ratios. Studies included evaluation of surfactant mixtures in lab tests determined to be suitable for Bakken reservoir conditions, showing good stability, interfacial tension behavior, and contact angles with improved rock wettability. While surfactants were evaluated via simulations, ultimately the field pilot was simplified to use only produced gas and water with no additives. After completing the injection scheme, a reservoir surveillance program, including continuous monitoring of the HnP well, was implemented to evaluate oil production performance and reservoir response.

The WAG injection scheme was completed with no injectivity issues with approximately 70 million standard cubic feet of produced gas and 23 Mbbls of produced water injected. Production in the offset wells showed no indication of a major gas breakthrough compared to earlier pilots. The use of WAG injection appeared to improve gas conformance and containment, delaying and reducing gas breakthrough. Additionally, no gas or water breakthrough was detected outside the pilot area of the 2560-acre drilling spacing unit. Initial production results show increased oil production in the HnP well with predicted trends in gas and water rates during flowback. Results include analysis and assessment of oil recovery and efficiency compared to simulation and the prior Bakken EOR pilots.
Results of a Project for the Electrification of Well Testing and Production Equipment in the Ecuadorian Amazon
Authors: Clerque B. , Gómez A. , Fernandez L. , Soria J.
Published: September 2024

The article presents the results and lessons learned from a project for the electrification of equipment used for well testing and production in Ecuador. We quantified the reduction of CO2 emissions and noise in upstream operations in some of the most active oil fields in South America, located in one of the most biologically rich places in the world. In Ecuador, jet pumping is used for well testing and to a lesser extent for mature field production. The power equipment for the jet pump includes a 350 hp diesel engine. To improve the energy efficiency of its services, a project was executed to replace these engines with electric motors in 30% of the production units of one of the largest service companies in Ecuador. CO2 emissions were quantified and noise generated by a sample of diesel engines were measured. From this data, the reduction of emissions achieved by this electrification project was estimated. The construction and operation of hydroelectric power plants have facilitated the integration of numerous oil fields in the Ecuadorian Amazon into the National Interconnected System. This has made it possible for operating and service companies to invest in projects to improve the energy efficiency of production processes. In this project, the replacement of diesel engines with electric motors has resulted in the avoidance of 11,200 tons of CO2 emissions since 2017. Costs associated with fuel supply were reduced. The electric equipment was more stable, and non-productive times were minimized. Additionally, noise levels in the external environment of the wells were lowered from 54 dBA to 41 dBA, this holds significant importance in environmentally sensitive regions, such as the Amazon. Among the lessons learned is the need for specialized personnel to operate and maintain electrical equipment. Internal certification processes were implemented for the technicians in charge of these tasks. It is not possible to change 100% of the production units because not all wells have access to electricity generated through renewable means. However, switching to electric motors has proven to be a cost-effective investment to improve the energy efficiency of operations and reduce CO2 emissions. It is known that to achieve climate change mitigation goals by 2050, the oil industry must reduce its emissions by 90%. The present work is an example of how current technologies, that leverage local government investments in renewables, make it possible to achieve substantial reductions in emissions, controlling the environmental impact in one of the most sensitive areas of the planet.
Are There Opportunities for Upstream Sector in Peru? A Comprehensive Study of Peruvian Peak Oil Production with Hubbert Model
Authors: Huerta V. A. , Morán A. , Adriano L. , Quintero E
Published: September 2024

Oil and gas industry in Peru has a long history with more than 100 years oil production with almost all coming from Talara and Marañón Basins. Peruvian oil production has decreased significantly up to 41 MBOPD in the latest 10 years1, with a cumulative oil production exceeding 1.6 and 1.1 billion barrels in Talara and Marañón fields2 respectively. Despite, primary recovery factors represent just 9% and 20% respectively. which may be an indicator of proved hydrocarbon potential that can be recovered with new development strategies and better technology.

This study explores the opportunities to increase the oil supply for the Upstream Sector in Peru by using the Hubbert Model to determine the peak oil and maturity degree of each production region, and for the whole country; a sound estimation of hydrocarbon potential would be crucial to trade off the negative impact in Peru's balance of payments and exchanges due to decreasing oil production and increasing oil imports.

A multicycle Hubbert approach was used to model production history, determine the peak oil and maturity degree, and forecast oil supply for each production region in Peru. Based on analysis of production trends, reserves and contingent resources can be identified in each area, as well as the more suitable technologies and development plans can be proposed.

Typically, primary recovery factors for main fields in Peru are in between 5 to 50% with 14% average. The multicycle Hubbert approach indicates that heavy oil recovery can be increased by at least 20% in Marañón Basin, with new technology (horizontal drilling with autonomous inflow control devices and/or selective completion and/or chemical EOR).

On the other hand, mature Talara fields have shown in recent years low decline rates which also indicates that additional development plans, with more suitable technologies for tight reservoirs, along with new waterflooding or gas flooding projects may also raise recovery factors up to 15%.
Heat Extraction Through an Advanced Closed-Loop Geothermal System
Authors: Liu S. , Taleghani A. Dahi
Published: September 2024

Heat production through conventional closed-loop geothermal systems (CLGSs) is constrained by the limited contact area available for heat exchange between rock formations and the wellbore containing circulating fluid. To address this challenge, an advanced closed-loop geothermal system (ACGS) has been proposed to enhance heat production in this research. The ACGS incorporates a hydraulic fracture, partitioned by a horizontal insulator for vertical zonal isolation of fluid flow in the fracture, into the closed-loop system's fluid circulation. Since working fluid flows through the partitioned fracture, convective heat transfer from rock to fluid in the fracture having a large surface area is introduced to the closed-loop system, which will significantly enhance the temperature of fluid produced from the system. To accurately assess the heat production performance of the ACGS, a comprehensive numerical study is performed. Initially, a three-dimensional hydrothermal model of the ACGS is developed and numerically validated. This numerical model is utilized to simulate heat production through the ACGS incorporating a double-wing fracture for different key parameters, including fracture dimensions and tubing thermal conductivity. Then, heat production performances of two main ACGS configurations respectively incorporating a branched fracture and a multiple-wing fracture are analyzed. Lastly, simulation results of the ACGS under different conditions were compared to determine the design parameters for ACGS yielding the highest heat production performance. Compared with the scenario without a fracture, the near-wellbore temperature of the ACGS has decreased significantly, indicating that the geothermal reservoir is cooled much more efficiently. Due to incorporation of a double-wing fracture, the cumulative extracted heat of a closed-loop system over 20 years is enhanced by up to 162.94%. Increasing the fracture half-length and fracture height can both enhance heat production efficiency of the ACGS considerably. Vacuum-insulated tubing with extremely low thermal conductivity performs better than polymeric insulation tubing in avoiding heat loss through tubing. Compared with a multiple-wing fracture, a branched fracture results in better heat production through the ACGS, with a larger number of fracture branches leading to more efficient heat production. A branched fracture can improve the cumulative extracted heat of a closed-loop system over 20 years by up to 321.77%. Therefore, the proposed ACGS emerges as a promising solution to overcome the limitations faced by closed-loop systems in heat production.
Economic Analysis of Lithium and Salts Recoveries from Bakken Formation
Authors: Jakaria Md , Ling Kegang , Wang Dongmei , Crowell Josh , Zheng Danzhu
Published: September 2024

Oilfield-produced water, a byproduct of extracting petroleum, often contains salts and other significant critical elements like lithium. Utilizing these elements provides a chance to enhance revenue streams in the petroleum industry while also tackling sustainability challenges. This research provides a thorough economic analysis of the extraction of critical elements, specifically lithium, from produced water in the Bakken Formation using the NanoLiSal recovery model. The research aims to evaluate the expenses of project design, anticipate revenue, outline implementation processes, and analyze the environmental impacts. Research utilizes economic analysis strategies to assess the NanoLiSal approach while offering future strategies for applying it. Essential elements comprise financial modeling, investigation of industry trends, and assessments of environmental impact. Economic analysis results provide valuable information on the viability of the recovery process, emphasizing patterns, obstacles, and potential advantages. This article presents a detailed and subtle comprehension of the economic environment, offering direction for future research and real-world implementations. It offers a well-defined project framework that includes the overall expenditure and income generated by the NanoLiSal recovery process. Results emphasize the possibility for the petroleum industry to increase revenue from disposable wastewater while also contributing to sustainability goals. This paper makes a substantial contribution by clarifying economic factors and offering practical insights for individuals and organizations involved in the sector.
Integrated Thermo-Hydro-Chemo-Mechanical (THCM) Analysis for Evaluating CO2 Plume Geothermal System Potential
Authors: Elsayed Touka , Okoroafor Esuru Rita
Published: September 2024

Objectives/Scope: The objective of this study is to conduct a thorough investigation into CO₂ Plume Geothermal (CPG) systems using a Thermo-Hydro-Chemo-Mechanical (THCM) model. It aims to unravel the complexities of supercritical CO₂ interactions within geothermal formations and to evaluate the parameters critical to optimizing geothermal output. Additionally, the research intends to refine the predictive capabilities of current models, ensuring a more accurate assessment of the geothermal potential and contributing to the sustainable development of geothermal resources.

Methods, Procedures, Process: Utilizing an integrative THCM model, we assessed the role of supercritical CO₂ in geothermal energy extraction. Geological, chemical, and mechanical parameters were meticulously quantified to simulate subsurface scenarios impacting the injectivity and productivity of CPG systems. Key chemical interactions and their impact on rock integrity were also modeled, incorporating acidification and carbonation reactions and their effect on rock strength and reservoir behavior under varying stress conditions. These procedures enable a comprehensive understanding of the multifaceted influences on CPG system efficiency.

Results, Observations, Conclusions: The study revealed that geochemical reactions, including acidification and carbonation, could alter rock integrity and affect the mechanical stress within the reservoir. These findings suggest that a careful balance of chemical and mechanical factors is essential for the optimal functioning of CPG systems.

Novel/Additive Information: This research introduces a novel THCM model, incorporating a more nuanced simulation of geothermal reservoirs, particularly in the context of supercritical CO₂ injection. By identifying the key parameters that govern the system's efficiency, this study contributes new insights to the field of renewable energy and informs better reservoir management strategies.
Use of Machine Learning in Microseismic Monitoring for Thermal Operations in Cold Lake, AB, Canada
Authors: Costin S. O. , Scaini S. , Zhao H. , Fink T. K. , Brisco C. , Feng J. , Yadav D. , Sidhu S.
Published: September 2024

This paper presents a novel advancement in the microseismic data processing workflow for monitoring thermal operations at an unconventional bitumen reservoir in Cold Lake, Alberta, Canada. The new enhancement to the monitoring workflow enables fast processing of the microseismic data, by filtering upwards of 94% of noise files from the daily dataset before the files are reviewed and analyzed. The software consists of a deep learning-based application trained to recognize noise files and classify event files according to their predominant source. The tool was deployed in production on an Azure-hosted data manager for almost a year, after extensive testing and achieving adequate level of performance.