Search Results (31)

Naturally fractured reservoirs (NFRs) account for a significant portion of the world’s oil and gas reserves. Among them, corridor-type NFRs, characterized by discrete fracture corridors, exhibit complex flow behavior that challenges conventional development strategies and reduces recovery efficiency. A review of previous studies indicates that failing to identify these corridors often leads to suboptimal recovery, whereas correctly detecting and utilizing them can significantly enhance production. This study introduces a well-testing technique designed to identify fracture corridors and to evaluate well placement for improved recovery prediction. A simplified modeling framework is developed, combining a local model for matrix/fracture wells with a global continuous-media model representing the corridor network. Diagnostic pressure and derivative plots are used to estimate corridor properties—such as spacing and conductivity—and to determine a well’s location relative to fracture corridors. The theoretical analysis is supported by numerical simulations in CMG, which confirm the key diagnostic features and flow regime sequences predicted by the model. The results show that diagnostic patterns can be used to infer fracture corridor characteristics and to approximate well positions. The proposed method enables early-stage structural interpretation and supports practical decision-making for well placement and reservoir management in corridor-type NFRs. Full article

With the development of tracer technology and the improvement of fine management in oil fields, chemical tracer monitoring is widely used to analyze the production profiles in commingled wells and horizontal wells. However, most existing tracer technologies can only determine the production profile and cannot calculate the water cut. This paper proposes an intelligent slow-release chemical tracer monitoring technology and a corresponding interpretation methodology, which can quantify the oil and water production rates and dynamically analyze the water cut of production profiles by simultaneous deployment of oil-soluble and water-soluble tracers. To validate this approach, this method was applied to well A of the Bohai Oilfield. The results showed that the calculation model based on produced tracer concentration can quantitatively determine the production profile and water cut of the monitored well. During the stable production period, Well A exhibited high production rates and a low water cut, and the contribution of oil production varied greatly among different layers. The first and third sections were identified as the main contributors, accounting for 51.8% and 23.2% of production, respectively, while the second and fourth sections showed lower contributions of 15.1% and 9.9%. The water cut of each section was below 30%. This intelligent slow-release tracer monitoring technology allowed for continuous production profiles in the monitored well. The proposed method provides effective guidance for characterizing the production profile and water flooding patterns of each layer. It is helpful for the efficient development of oil and gas reservoirs. Full article

The study aims to improve the efficiency of oil field development at the Kalamkas field through the implementation of new methods for analyzing hydrodynamic survey data and monitoring well conditions. It is hypothesized that the use of integrated geophysical and hydrodynamic methods will enhance forecasting accuracy, optimize field operations, and increase the hydrocarbon recovery factor. An integrated approach combining pulsed neutron logging (PNL), acoustic cementometry (AC), inflow and injectivity profile evaluation methods, and specialized software for advanced data interpretation was applied, significantly improving the accuracy of well condition analysis. The analysis enabled the identification of oil and gas saturation intervals, zones of increased water cut, and cementing defects in casing, and allowed for a quantitative assessment of reservoir permeability dynamics. Hydraulic fracturing application resulted in a 10–15% increase in permeability in certain zones, with an average oil recovery factor increase of 5%. Analysis of PNL data demonstrated the transition of oil-saturated reservoirs to water saturation during development, confirmed by geophysical and pressure build-up survey results. The study identified the primary causes of increased water cut and key factors leading to production rate decline. Proposed measures for optimizing operating modes and well grid efficiency contribute to improving existing field management practices. Full article

The cornerstone of the global economy, oil and gas reservoir development, faces numerous challenges such as resource depletion, operational inefficiencies, safety concerns, and environmental impacts. In recent years, the integration of artificial intelligence (AI), particularly artificial general intelligence (AGI), has gained significant attention for its potential to address these challenges. This review explores the current state of AGI applications in the oil and gas sector, focusing on key areas such as data analysis, optimized decision and knowledge management, etc. AGIs, leveraging vast datasets and advanced retrieval-augmented generation (RAG) capabilities, have demonstrated remarkable success in automating data-driven decision-making processes, enhancing predictive analytics, and optimizing operational workflows. In exploration, AGIs assist in interpreting seismic data and geophysical surveys, providing insights into subsurface reservoirs with higher accuracy. During production, AGIs enable real-time analysis of operational data, predicting equipment failures, optimizing drilling parameters, and increasing production efficiency. Despite the promising applications, several challenges remain, including data quality, model interpretability, and the need for high-performance computing resources. This paper also discusses the future prospects of AGI in oil and gas reservoir development, highlighting the potential for multi-modal AI systems, which combine textual, numerical, and visual data to further enhance decision-making processes. In conclusion, AGIs have the potential to revolutionize oil and gas reservoir development by driving automation, enhancing operational efficiency, and improving safety. However, overcoming existing technical and organizational challenges will be essential for realizing the full potential of AI in this sector. Full article

Reducing atmospheric emissions through the introduction of circular economy principles is one of the current tasks of sustainable regional development. The purpose of this research is to study the impact of the actions taken by Karachaganak Petroleum Operating B.V. (KPO) to reduce air pollution, and, based on this, to evaluate the potential of forming a circular economy in one of the biggest regions of Kazakhstan in which KPO is operating. The air pollution in the region is related to the oil and gas production activities of the company. This study was conducted using econometric modeling and statistical and comparative analyses. This study’s value lies in its interdisciplinary approach, which made it possible to combine environmental and economic criteria for sustainable regional development with the features of emissions and waste management technologies within the industry. Studying the production activities and analyzing the impact of KPO on the development of the region in the period from 2012 to 2022 made it possible to construct a matrix of the restorative potential of a circular economy in the region. A model for the formation of a circular economy was proposed, which is based on the introduction of innovations, investments in environmental protection, and the use of the best available technologies for reinjecting gas into the reservoir, increasing energy efficiency, and recycling waste, which resulted in a significant (2.2 times) reduction in the amount of air pollution in the region. According to the forecast model (2024–2028), it was determined that, in the case of maintaining certain independent indicators’ dynamics of development, the level of atmospheric emissions by KPO could be reduced by two times. The results of this work prove that further studies on the problems associated with reducing atmospheric pollution within the framework of the formation of a circular economy are quite promising. Additionally, the results of this study are interesting and may be useful for the implementation of measures to manage air quality in the region by managers, heads of organizations, state and local authorities, and researchers interested in promoting the Environmental Social Governance (ESG) concept of sustainable development. Full article

Accurate oil and gas production forecasting is essential for optimizing field development and operational efficiency. Steady-state capacity prediction models based on machine learning techniques, such as Linear Regression, Support Vector Machines, Random Forest, and Extreme Gradient Boosting, effectively address complex nonlinear relationships through feature selection, hyperparameter tuning, and hybrid integration, achieving high accuracy and reliability. These models maintain relative errors within acceptable limits, offering robust support for reservoir management. Recent advancements in spatiotemporal modeling, Physics-Informed Neural Networks (PINNs), and agent-based modeling have further enhanced transient production forecasting. Spatiotemporal models capture temporal dependencies and spatial correlations, while PINN integrates physical laws into neural networks, improving interpretability and robustness, particularly for sparse or noisy data. Agent-based modeling complements these techniques by combining measured data with numerical simulations to deliver real-time, high-precision predictions of complex reservoir dynamics. Despite challenges in computational scalability, data sensitivity, and generalization across diverse reservoirs, future developments, including multi-source data integration, lightweight architectures, and real-time predictive capabilities, can further improve production forecasting, addressing the complexities of oil and gas production while supporting sustainable resource management and global energy security. Full article

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development. Full article

The productivity prediction of weakly volatile fractured reservoirs is influenced by reservoir parameters and fluid characteristics. To address the computational challenges posed by complex fractures, an equivalent fracture element method is proposed to calculate equivalent permeability in fractured zones. A three-phase seepage model based on material balance is developed, using the Baker linear model to determine the relative permeabilities of oil, gas, and water while accounting for bound water saturation. Dynamic drainage distance and conductivity coefficients are introduced to calculate production at each stage, with the semi-analytical model solved iteratively for pressure and saturation. Validation against commercial simulation software confirms the model’s accuracy, enabling the construction of productivity curves and analysis of reservoir characteristics and injection scenarios. Results showed that the equivalent fracture element method effectively handled multiphase nonlinear seepage and predicted productivity during water flooding. Productivity was more sensitive to through-fracture models, with production increasing as the fracture extent expanded. Optimal water injection occurred when the formation pressure dropped to 80% of the bubble point pressure, and the recovery efficiency improved with periodic-injection strategies compared to conventional methods. These findings have significant implications for improving oil recovery, optimizing injection strategies, and advancing the design of efficient reservoir management techniques across scientific, practical, and technological domains. Full article

Scaling is a significant challenge in oilfield production gathering and transportation stations, and it not only constrains the economic efficiency but also affects the development of oil and natural gas. This study proposes a scaling prediction model based on chemical experimental analysis and reservoir dynamic analysis methods for the gathering and transportation stations in the Changqing Oilfield. The objective of this study is to provide technical support for the oilfield to advance precise management and achieve cost reduction and efficiency enhancement. Initially, the water quality and scale samples of the oilfield were tested and analyzed using Inductively Coupled Plasma (ICP), Ion Chromatography (IC), and Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS), and the distribution and patterns of scaling in the gathering and transportation pipelines were studied. Based on this, using the test data and the production liquid ratio of each development layer at the gathering and transportation stations, a reservoir dynamic correlation method was employed to construct a prediction model for the development layer with the highest similarity to the tested water samples at the stations and the types of scale samples. The results indicate that this prediction method can effectively reduce the scaling rate and provide guidance for the anti-scaling process in the Changqing Oilfield. Full article

Reservoir reconstruction is a critical challenge in many significant underground energy projects, such as enhanced geothermal systems, oil shale extraction, and shale gas development. Effectively reconstructing geothermal reservoirs can significantly enhance the exploitation and production capacity of geothermal resources. However, this process requires stringent technical standards and varies with different geological conditions across regions, necessitating tailored reconstruction strategies. This review offers a comprehensive examination of hydraulic fracturing within geothermal reservoirs, covering the geological and physical characteristics inherent to these systems, the effects of injection methods and thermal stimulation on hydraulic fracturing processes, and the assessment and optimization of transformation effects, as well as environmental implications and risk management considerations. We explore the influence of various injection modes on hydraulic fracturing dynamics. Moreover, we compare the differences between hydraulic fracture propagation with and without thermal effects. Additionally, we summarize optimization strategies for reservoir reconstruction. Finally, we discuss several challenges and potential future directions for development, offering insights into possible advancements. This review is of substantial significance for both research and commercial applications related to hydraulic fracturing in geothermal reservoirs. Full article

This study investigates the complex dynamics of retrograde condensation in condensate gas reservoirs, with a particular focus on the challenges posed by retrograde condensate pollution, which varies in condensate oil content and impacts on reservoir productivity. Numerical simulations quantify the distribution of condensate oil and the reduction in gas-phase relative permeability in reservoirs with 100.95 g/m³, 227.27 g/m³, and 893.33 g/m³ of condensate oil. Unlike previous studies, this research introduces an orthogonal experiment to establish a methodology for studying the dynamic sensitivity factors across different types of gas reservoirs and various development stages, systematically evaluating their contributions to condensate oil. The analysis reveals that reservoirs with low to moderate condensate oil content gradually experience expanding polluted regions, affecting long-term production. The maximum condensate saturation near the wellbore can reach 0.19, reducing gas-phase relative permeability by about 25.44%. In contrast, high-condensate oil reservoirs show severe early-stage retrograde condensation, with saturations up to 0.35 and a permeability damage rate reaching 73%. The orthogonal experiments identify reservoir permeability and condensate oil content as critical factors influencing production indicators. The findings provide key insights and practical recommendations for optimizing production strategies, emphasizing tailored approaches to mitigate retrograde condensation and enhance recovery, especially in high-condensate oil reservoirs, offering theoretical and practical guidance for improving reservoir management and economic returns. Full article

Offshore oil and gas fields pose significant challenges due to their lower accessibility compared to onshore fields. To enhance operational efficiency in these deep-sea environments, it is essential to design optimal fluid production conditions that ensure equipment durability and flow safety. This study aims to develop a smart operational solution that integrates data from three offshore gas fields with a dynamic material balance equation (DMBE) method. By combining the material balance equation and inflow performance relation (IPR), we establish a reservoir flow analysis model linked to an AI-trained production pipe and subsea pipeline flow analysis model. We simulate time-dependent changes in reservoir production capacity using DMBE and IPR. Additionally, we utilize SLB’s PIPESIM software to create a vertical flow performance (VFP) table under various conditions. Machine learning techniques train this VFP table to analyze pipeline flow characteristics and parameter correlations, ultimately developing a model to predict bottomhole pressure (BHP) for specific production conditions. Our research employs three methods to select the deep learning model, ultimately opting for a multilayer perceptron (MLP) combined with regression. The trained model’s predictions show an average error rate of within 1.5% when compared with existing commercial simulators, demonstrating high accuracy. This research is expected to enable efficient production management and risk forecasting for each well, thus increasing revenue, minimizing operational costs, and contributing to stable plant operations and predictive maintenance of equipment. Full article

The increasing level of anthropogenic CO₂ in the atmosphere has made it imperative to investigate an efficient method for carbon sequestration. Geological carbon sequestration presents a viable path to mitigate greenhouse gas emissions by sequestering the captured CO₂ deep underground in rock formations to store it permanently. Geochemistry, as the cornerstone of geological CO₂ sequestration (GCS), plays an indispensable role. Therefore, it is not just timely but also urgent to undertake a comprehensive review of studies conducted in this area, articulate gaps and findings, and give directions for future research areas. This paper reviews geochemistry in terms of the sequestration of CO₂ in geological formations, addressing mechanisms of trapping, challenges, and ways of mitigating challenges in trapping mechanisms; mineralization and methods of accelerating mineralization; and the interaction between rock, brine, and CO₂ for the long-term containment and storage of CO₂. Mixing CO₂ with brine before or during injection, using microbes, selecting sedimentary reservoirs with reactive minerals, co-injection of carbonate anhydrase, and enhancing the surface area of reactive minerals are some of the mechanisms used to enhance mineral trapping in GCS applications. This review also addresses the potential challenges and opportunities associated with geological CO₂ storage. Challenges include caprock integrity, understanding the lasting effects of storing CO₂ on geological formations, developing reliable models for monitoring CO₂–brine–rock interactions, CO₂ impurities, and addressing public concerns about safety and environmental impacts. Conversely, opportunities in the sequestration of CO₂ lie in the vast potential for storing CO₂ in geological formations like depleted oil and gas reservoirs, saline aquifers, coal seams, and enhanced oil recovery (EOR) sites. Opportunities include improved geochemical trapping of CO₂, optimized storage capacity, improved sealing integrity, managed wellbore leakage risk, and use of sealant materials to reduce leakage risk. Furthermore, the potential impact of advancements in geochemical research, understanding geochemical reactions, addressing the challenges, and leveraging the opportunities in GCS are crucial for achieving sustainable carbon mitigation and combating global warming effectively. Full article

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. Full article

In the development of the Qianshao (QS) gas condensate reservoir, it is crucial to consider the phenomenon of retrograde condensation. Understanding the condensate saturation distribution with respect to time and space within the reservoir is essential for planning and implementing effective strategies for the future development of the QS gas condensate reservoir. In this paper, various PVT experiments (including reservoir oil recombination, flash separation, constant composition expansion, and constant volume depletion) were conducted to study the PVT properties and phase behavior of QS gas condensate fluid. Based on experimental data, our in-house PVT computation package was used to determine the appropriate EOS model parameters for the QS gas condensate. A four-step reservoir fluid characterization procedure and workflow for gas condensate reservoirs was developed. Furthermore, by analyzing the pressure-temperature phase envelope, the maximum possible condensate saturation in the QS well area was estimated to be around 3%. Numerical reservoir simulation models were developed using both the EOS model and actual reservoir engineering data. These simulation models were specifically designed to replicate the retrograde condensation process that occurs during production, taking into account both vertical and horizontal wells. By simulating the production process, these single-well reservoir simulation models enable us to quantitatively evaluate the condensate saturation and its distribution over space and time within a specific control area around a single well. Reservoir simulation results show that the condensate build-up around vertical and horizontal wells is quite different. For a vertical well, the maximum condensate oil saturation (30%) around the wellbore is located approximately 5 to 6 m from the well’s center. In contrast, the horizontal well model demonstrates a maximum condensate saturation of no more than 1.5%. This information is crucial for making informed decisions regarding the effective development and management of the QS gas condensate reservoir. Full article