Reservoir Engineering Research Articles

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Conceptual advances in petrophysical inversion techniques: The synergy of machine learning and traditional inversion models

Petrophysical inversion techniques are essential for estimating reservoir properties such as porosity, permeability, and fluid saturation, which are critical for optimizing hydrocarbon recovery and reservoir management. Traditionally, deterministic and stochastic inversion methods have relied on well log data, core samples, and seismic information to generate models of subsurface properties. However, these techniques face limitations in handling large datasets, nonlinear relationships, and uncertainties in complex reservoir environments. Recent advances in machine learning (ML) offer an opportunity to enhance these traditional methods by providing a data-driven approach capable of learning patterns and refining inversion models. This review explores the potential synergy between traditional petrophysical inversion techniques and machine learning algorithms to create a new paradigm for reservoir parameter estimation and analysis. Machine learning’s ability to process large volumes of diverse data and capture nonlinear relationships complements the physics-based constraints of traditional inversion methods. By combining these two approaches, hybrid models can provide improved accuracy in predicting reservoir properties and handling data gaps or uncertainties. The review discusses specific case studies where machine learning-assisted inversion models have been successfully applied, such as the estimation of porosity and permeability, seismic inversion for lithology identification, and fluid saturation modeling. These hybrid models demonstrate enhanced predictive accuracy, faster computational efficiency, and real-time adaptability, transforming reservoir characterization and management. The integration of machine learning with traditional inversion techniques represents a major advancement in petrophysical analysis. This synergy holds the potential to revolutionize reservoir parameter estimation, making it more accurate, robust, and responsive to real-time data, ultimately improving decision-making in reservoir engineering and maximizing hydrocarbon recovery. Keywords: Petrophysical Inversion, Machine Learning, Traditional Inversion, Models.

Geoscience spillover: Gunnar Böðvarsson and the adoption of petroleum technologies in Iceland’s geothermal industry, 1940s–1970s

ABSTRACT During the twentieth century, Iceland became a leader in geothermal energy use, with hot water and steam providing for the heating of 9 out of 10 houses and almost one-third of the electricity supply. A crucial factor in the expansion of geothermal energy between the 1940s and 1970s were indirect ‘spillovers’ from the oil industry via the applied geosciences. While fundamentally different in end use, geothermal and hydrocarbon industries employ similar geoscientific methods for exploring and extracting fluids and gases. This article traces the spillovers from oil to geothermal by examining Iceland’s Geothermal Division and its founding director, Gunnar Böðvarsson. He saw the oil industry as a model and embraced its geoscientific exploration methods, oilwell rotary drilling and petroleum reservoir engineering. Adapted to the requirements of geothermal energy, petroleum technologies greatly improved the knowledge of geothermal reservoirs and the efficiency of well drilling and hot water and steam extraction. The geoscience spillover between oil and geothermal reveals an understudied dimension of geothermal history and the crucial role of the geosciences at the intersection of academia and energy industries, which also resulted in reverse spillovers from geothermal to oil and complicates the common dichotomization of fossil fuels vs. renewable alternatives.

Three-dimensional numerical study on reservoir sediment flushing through an initially covered bottom tunnel

Sediment flushing through bottom tunnels has been widely applied in engineering practice to alleviate reservoir sedimentation. However, there is limited understanding of whether flushing can be accomplished once the tunnel intake is covered by deposited sediment. Here, a three-dimensional model is used to resolve the hydro-sediment-morphodynamic processes and reveal the physical mechanism for the occurrence of reservoir sediment flushing through an initially covered bottom tunnel. The results demonstrate that the reservoir water level and initial cover layer thickness have a significant impact on the pressure difference between tunnel inlet and outlet and the resistance of tunnel walls, determining the momentum of the water-sediment mixture. If water level is low or cover layer is thick, the pressure is not sufficient to overcome the resistance. The momentum tends to zero, and tunnel blockage occurs. If water level is high or cover layer thickness is small, the scour hole depth will exceed the cover layer thickness and the sediment concentration in tunnel decreases. The resistance is reduced significantly, and sediment flushing is accomplished. The present work facilitates enhanced understanding of sediment flushing, thereby informing reservoir engineers and managers of potential approaches to benefit maximization and sustainable sediment management.

Estimation of the depth-variant seismic wavelet based on the modified unscaled S-transform

Seismic inversion is one of the key techniques used for reservoir characterization. Depth-domain seismic inversion eliminates the cumulative errors associated with depth-to-time and time-to-depth conversions, thus providing geologists and reservoir engineers with an intuitive basis for geological interpretation. The method has received increasing attention in the field of reservoir characterization. Extracting accurate depth-domain seismic wavelets is a prerequisite for successful depth-domain seismic inversion. However, the depth-domain wavelet is velocity-dependent and exhibits significant non-stationarity, which leads to the failure of seismic wavelet estimation methods based on the stationary convolutional model. To this end, we propose a modified wavenumber-domain unscaled S-transform (MWUST) method to accomplish accurate estimation of depth-domain seismic wavelets. The proposed method enhances the accuracy of wavenumber components by removing the linear wavenumber-dependent term from the S-transform. Furthermore, it introduces slope and intercept parameters to improve the depth resolution at low wavenumbers, thereby yielding a more reliable depth–wavenumber spectrum. Subsequently, the relationship between the non-stationary depth-domain seismic wavelet and the depth–wavenumber spectrum is established, allowing for the accurate extraction of non-stationary wavelets under the assumption that the depth-domain reflectivity is a random sequence. Synthetic and real data applications have been used to verify the effectiveness of the proposed method.

Sensitivity analysis of a dual-continuum model system for integrated CO2 sequestration and geothermal extraction in a fractured reservoir

Depleted hydrocarbon reservoirs are considered as the most feasible option for CO2 geological sequestration and utilization. Most of the hydrocarbon reservoirs are naturally fractured. Simulation of fluid flow and heat transfer in these fractured formations remains a significant challenge in reservoir engineering. In this study, a dual-continuum model is developed to simulate integrated CO2 sequestration and CO2-circulated geothermal extraction in a fractured reservoir block in North Oman. The high-dimensional sensitivity of key parameters controlling CO2-brine flow and heat transfer in this matrix-fracture system is quantitatively evaluated by an efficient surrogate modeling approach. The surrogate models are constructed and validated based on a suite of physics-based model simulations. It is found that fracture permeability dominates the CO2 injectivity, storage, circulation and associated geothermal extraction. Response surface analysis shows that the flow area density between matrix-fracture and matrix block length controls the flux interaction between matrix and fracture formations. In contrast, the fracture aperture shows negligible influence in the dual-continuum modeling system. Particularly, sensitivity varying with locations on the response surface is analyzed for defined performance indicators.

Universal Time-Entanglement Trade-Off in Open Quantum Systems

We demonstrate a surprising connection between pure steady-state entanglement and relaxation time scales in an extremely broad class of Markovian open systems, where two (possibly many-body) systems, A and B, interact locally with a common dissipative environment. This setup also encompasses a broad class of adaptive quantum dynamics based on continuous measurement and feedback. As steady-state entanglement increases, there is generically an emergent strong symmetry that leads to a dynamical slow-down. Using this, we can prove rigorous bounds on relaxation times set by steady-state entanglement. We also find that this time must necessarily diverge for maximal entanglement. To test our bound, we consider the dynamics of a random ensemble of local Lindbladians that support pure steady states, finding that the bound does an excellent job of predicting how the dissipative gap varies with the amount of entanglement. Our work provides general insights into how dynamics and entanglement are connected in open systems and has specific relevance to quantum reservoir engineering. Published by the American Physical Society 2024

Geological and Engineering Integration Fracturing Design and Optimization Study of Liushagang Formation in Weixinan Sag

The Weixinan Sag in the Beibuwan Basin is rich in shale oil resources. However, the reservoirs exhibit rapid phase changes, strong compartmentalization, thin individual layers, and high-frequency vertical variations in the thin interbedded sandstone and mudstone. These factors can restrict the height of hydraulic fracture propagation. Additionally, the low-porosity and low-permeability shale oil reservoirs face challenges such as low production rates and rapid decline. To address these issues, the Plannar3D full 3D fracturing model was used to simulate hydraulic fracture propagation and to study the main controlling factors for fracture propagation in the second member of the Liushagang Formation. Based on the concept of geological–engineering integration, a sweet spot evaluation was conducted to identify reservoirs with relatively better brittleness, reservoir properties, and oil content as the fracturing targets for horizontal wells. The UFM model was then applied to optimize fracturing parameters. This study indicates that the matrix-type oil shale has a high clay mineral content, resulting in a low Young’s modulus and poor brittleness. This makes hydraulic fracture propagation difficult and leads to less effective reservoir stimulation. In contrast, hydraulic fractures propagate more easily in high-brittleness interlayer-type oil shale. Therefore, it is recommended to prioritize the extraction of shale oil from interlayer-type oil shale reservoirs. The difference in interlayer stress is identified as the primary controlling factor for cross-layer fracture propagation in the study area. Based on the concept of geological–engineering integration, a sweet spot evaluation standard was established for the second member of the Liushagang Formation, considering both reservoir quality and engineering quality. Four sweet spot zones of interlayer-type oil shale reservoirs were identified according to this evaluation standard. To achieve uniform fracture initiation, a differentiated segment and cluster design was implemented for certain high-angle sections of well WZ11-6-5d. Interlayer-type oil shale was selected as the fracturing target, and the UFM was used for hydraulic fracture propagation simulation. Fracturing parameters were optimized with a focus on hydraulic fracture characteristics and the estimated ultimate recovery (EUR). The optimization results were as follows: a single-stage length of 50 m, cluster spacing of 15 m, pump injection rate of 10 m3/min, fluid intensity of 25 m3/m, and proppant intensity of 3.5 t/m. The application of these optimized fracturing parameters in field operations resulted in successful fracturing and the achievement of industrial oil flow.

The Identification and Quantification of Hidden Hazards in Small Scale Reservoir Engineering Based on Deep Learning: Intelligent Perception for Safety of Small Reservoir Projects in Jiangxi Province

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding–decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by unmanned aerial vehicles (UAVs). The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) were obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers) proposed in this paper outperforms other methods. The main evaluation indicator, mIoU of HHSN-25, is higher than other methods, reaching 87.00%, and the Unet is 85.50%, and the Unet++ is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

Exploration and Practice of Synergistic and Efficient Development Technology for Gas-Capped Condensate Gas Reservoirs and Oil Rings

Abstract The oil-ringed condensate gas reservoir is one of the more unique and complex types of oil and gas reservoirs. The stable oil and gas interface is crucial for the efficient development of such reservoirs, and the fundamental means of addressing the issue lies in maintaining the dynamic balance between the gas cap and the oil ring. In this paper, we address issues such as the rapid decline in oil ring production, gas cap breakthrough, and increased condensate oil loss exposed during the development of the R carbonate reservoir in the Caspian Sea Basin of Kazakhstan. Using reservoir engineering and numerical simulation methods, we optimized synergistic development ideas and the boundaries of exploitation policies for gas-capped condensate gas reservoirs and oil rings. We formulated reasonable development parameters such as extraction rate, injection-production ratio, barrier water injection ratio, and proposed dynamic control standards for zoning and classification. After implementing the plan, the gas-oil ratio of the oil ring significantly decreased, with a reduction rate of 12 percentage points. The gas production per unit pressure drop increased by 40%. The annual decline rate of condensate oil decreased from 23.6% to 13.8%. All development indicators exhibited characteristics of benign development, providing a theoretical foundation and technical methods for guiding the efficient development of similar oil and gas reservoirs.

Feasibility Theory and Practice of CO2-EOR in High CO2-Content Near-saturated Edge-bottom Water Reservoir

Abstract CO2-EOR technology for low-permeability tight oil and gas reservoir is in the ascendant, and CO2-EOR technology for old oilfields has not been paid enough attention, but the implementation scale has great potential, and the expected effect and implementation conditions are good. The feasibility of CO2-EOR technology in the ultra-high water cut stage of high CO2 content, near saturation pressure and edge-bottom water reservoir was studied theoretically, and the field implementation effect was tracked and evaluated. Via petrophysics, reservoir engineering, and site case analysis, a relatively comprehensive reservoir numerical model was developed, clarifying the impact of periodic gas injection volume, gas injection rate, soak time and liquid production rate on the production effect of CO2 huff and puff were clarified. The key injection-production parameters of CO2 huff and puff test wells were designed, and the feasibility study progress of CO2-EOR in near-saturated edge-bottom water reservoirs with high CO2 content was summarized. The theoretical research shows that the change rules of gas-water dual-drive, CO2 water control and oil increase in the process of CO2 huff and puff in high CO2 near-saturated edge and bottom water reservoirs, and reveals the synergistic mechanism of CO2 huff and puff to suppress water and increase energy. The tracking evaluation shows that the sweep condition of CO2 after huff and puff in the test well is complex, the storage ratio is in the range of 40%-70%, the stage oil increase is in the range of 700t-3000t, and the optimized oil increase and oil change rate can reach 2.7t/t. Based on the comprehensive theory and practice, it is considered that CO2-EOR has good potential for high CO2 near-saturated edge and bottom water reservoirs under fine control. This study has guiding and reference significance for CO2-EOR technology and application in similar oilfields at home and abroad.

The Influence of Gas Lift on Oil Drainage Area in Reservoirs

Abstract Gas lift is a widely employed artificial lift method in the oil and gas industry, with the potential to significantly impact oil recovery from reservoirs. This study investigates the effect of gas lift on the oil drainage area within reservoirs, aiming to enhance our understanding of its role in reservoir management. Using advanced reservoir engineering and simulation techniques, the research explores the dynamics of gas lift and its influence on the accessibility of hydrocarbon resources. The study provides a comprehensive analysis of gas lift parameters, including gas injection rates, depths, and configurations, and their collective impact on the drainage area. It also considers the interplay between gas lift operations and reservoir characteristics such as permeability, porosity, and fluid properties. Findings from this study reveal that gas lift can effectively expand the drainage area by reducing bottomhole pressures and facilitating the movement of oil within the reservoir. This expansion enables the recovery of hydrocarbons from previously untapped regions, resulting in improved oil production. The research underscores the importance of optimizing gas lift design to maximize reservoir performance. Additionally, the study addresses operational challenges associated with gas lift, including gas allocation, injection optimization, and equipment reliability. It provides insights into the economic and operational considerations of gas lifts, offering valuable recommendations for reservoir management. The outcomes of this research contribute to a deeper understanding of how gas lift influences the oil drainage area in reservoirs, providing valuable insights for reservoir engineers, operators, and industry professionals involved in oil production. This knowledge supports informed decision-making to optimize oil recovery, reservoir performance, and the efficient use of gas lift technologies.

Research of productivity and interlayer interference mechanism of multilayer co-production in reservoirs with multi-pressure systems

Abstract Due to the differences of physical and fluid properties in vertical oil layers, the interlayer contradictions are prominent and affect the reservoir utilization degree seriously. Therefore, the coordination between reasonable bottom-hole flow pressure and optimal production, as well as the reduction of interlayer interference are the primary concerns during the co-production of multi-layer oil reservoirs. Based on the reservoir engineering and fluid mechanics in porous media theory, this work adopt the comprehensive pressure system and consider the interlayer interference to establish a multi-branch horizontal well productivity model and interlayer interference mathematical model. By analyzing the main controlling factors and interference mechanisms, this article establishes a pattern of interlayer interference, and improves the quantitative characterization interlayer interference theory in multi-layer combined mining. The study has shown that: (1) The interlayer interference is beneficial for balancing the production of different layers and improving development efficiency, it is greatly affected by interlayer heterogeneity; (2) When the number of layers exceeds 2 layers, the interference coefficient increases; With the increase of the layer thickness, the thicker oil layers have higher productivity, and the thickness of layer has a significant effect on the production of low-pressure layer; When the production pressure difference increases, the interlayer interference can be reduced; (3) The interlayer interference mathematical model constructed in this paper has high prediction accuracy and strong practicality, which has theoretical guidance significance for the division of strata in comprehensive adjustment of reservoirs.

Reservoir numerical simulation method considering time-varying relative permeability during polymer flooding process

Abstract In response to the abnormal decline in liquid production capacity during the middle and later stages of chemical flooding in Chinese offshore oil fields, in-depth analysis was conducted using reservoir engineering, dynamic analysis, and reservoir simulation methods. Because of the viscoelasticity of polymer solution, the relative permeability curve of oil/water during polymer flooding is different from that during water flooding, including the minimum mobile water saturation and residual oil saturation. In this study, an interpolation coefficient ‘f’ related to polymer solution concentration was firstly proposed for simplicity. Then, reservoir numerical simulation with the self-developed simulator ‘SIMFAST’ were conducted to investigate the influence of the interpolation coefficient ‘f’ on the injector and producer performance and the applications in actual field production history matching. The results indicated that compared with the commercial simulators, the self-developed simulator with the interpolation factor could more reliably describe the characteristic of the injected polymer solution in controlling water cut rise, and more efficiently simplify the combination of parameters on relative permeability curves during polymer flooding in offshore oilfields. Finally, above reservoir simulation method considering the change of the relative permeability curves during chemical flooding, was successfully conducted in the production history matching in J oilfield in Bohai Bay China, in terms of oil rate & water cut parameters. The research results give an efficient method to investigate the fluid seepage behaviour variation and its influence on oil rate and water cut during polymer flooding in China offshore reservoirs, which offers very useful technical support for the accurate design of chemical flooding schemes.

Research and Practice of Development Model during the Continental Heavy Oil Field’s Extra-high Water Cut Stage in the Bohai Sea

Abstract The formation mechanism, production limits and injection-production pattern of residual oil were studied by laboratory experiment, micropore simulation and reservoir engineering. The findings indicate that when injection times increase, the displacement efficiency rises as well. The residual oil mostly remains in the pore throat, which has a radius of 10 to 50 μ m, with a mean pore throat radius of 35 μ m.. As the displacement pressure gradient increases from 0.004MPa/m to 0.012MPa/m, When the displacement ratio of piston type increases and the displacement ratio of water film flow drops, the displacement efficiency grows. The horizontal well pattern’s gradient of displacement pressure is increased by 1.5 ~ 1.7 times compared with the oriented well pattern, and the displacement efficiency is increased by 12% ~ 15%. The development of the Bohai oilfield during the ultra-high water cut stage has been informed by the aforementioned findings, forming a horizontal well joint directional well three-dimensional pattern development model, oil production rate increased from 1.3% to 2.3%, and water drive recovery rate increased from 24.5% to 38.6%, realizing efficient development in the ultra-high water cut stage, and providing reference for the development of similar oil fields.

Optimization of The Development Plan for Ultra-low Permeability And Weak Volatility Reservoir

Abstract Block F1 of F oil field belongs to ultra-low permeability volatility reservoir. Compared with similar reservoirs at home and abroad, the reservoir property of this block is poor and the permeability is only 1.4 mD. There is no development experience for reference. It is urgent to explore the effective utilization technology of volatility reserves in F oil field. In view of the problems that the preliminary research results of F1 block cannot meet the development needs, the reservoir research is not deep, and the development rules are not clear, Through fine structural interpretation and reservoir prediction, the geological characteristics of the oil reservoir have been clarified; We used multiple indicators to evaluate the initial production capacity of the block, And clarified the initial development rules of the weak volatility reservoir. Perfect the dynamic analysis and production capacity evaluation technology of weak volatility reservoir. By using the reservoir engineering method, we optimized the reasonable working system and the development plan. So we expanded the potential of block development, adjustment and external expansion and optimized the development plan, then we constructed the rational well network deployment technology of weak volatility reservoirs which can provide guidance for the effective development of the F1 block. The results can provide reference for the development of other ultra- low permeability weak volatility reservoirs.

Construction and preliminary application of large language model for reservoir performance analysis

A large language model (LLM) is constructed to address the sophisticated demands of data retrieval and analysis, detailed well profiling, computation of key technical indicators, and the solutions to complex problems in reservoir performance analysis (RPA). The LLM is constructed for RPA scenarios with incremental pre-training, fine-tuning, and functional subsystems coupling. Functional subsystem and efficient coupling methods are proposed based on named entity recognition (NER), tool invocation, and Text-to-SQL construction, all aimed at resolving pivotal challenges in developing the specific application of LLMs for RDA. This study conducted a detailed accuracy test on feature extraction models, tool classification models, data retrieval models and analysis recommendation models. The results indicate that these models have demonstrated good performance in various key aspects of reservoir dynamic analysis. The research takes some injection and production well groups in the PK3 Block of the Daqing Oilfield as an example for testing. Testing results show that our model has significant potential and practical value in assisting reservoir engineers with RDA. The research results provide a powerful support to the application of LLM in reservoir performance analysis.

Research and Field Application on Extreme Subdivision Water Injection Technology for Low Permeability Reservoirs

Abstract Low permeability Block A is about to enter the development phase of extremely high water cut. Due to the low overall recovery degree and residual oil scattered sporadically, the efficient development meets more difficulty and conventional residual oil analysis and adjustment methods cannot fulfil the current needs of precise development. To enhance the reservoir utilization degree, we conduct extremely concise zonal water injection experiment from three aspects: sand body description, policies establishment, and proper technique application. In terms of sand body description, four types of sand-body distribution patterns were summarized through fine characterization of vertical, planar, and spatial single sand bodies, clarifying the origin and potential distribution of residual oil. In terms of development policy, numerical simulation and reservoir engineering methods have been comprehensively applied to determine the subdivision combination of injection layers and reasonable water allocation boundaries, and subdivision principles have been established, considering single septation to thin and thick layer with combining enhancing -controlling. In terms of supporting technology, we have studied the small space subdivision process of small compartments, optimized the testing and adjustment methods, and finally achieved accurate separation, strict clamping, and accurate matching of water injection layers. Through the research and practice, it realized the finest septation of the water injection to extremely enhance the subdivision degree and the efficiency of the injection. It also have strengthened the utilization of thin and poor layers and effectively controlled the decline.

Discussion on the development practice of low permeability reservoir of beach bar in saltwater lake basin—Take Wu5 block of Wunan Oilfield in Qaidam Basin as an example

Abstract Based on the analysis of field outcrops, drilling cores, casting thin sections, logging interpretation, and development characteristics in the southwestern region of the Qaidam Basin, combined with the sedimentary characteristics, development patterns, and injection production well networks of the Neogene saline lake facies beach and dam in the southwestern region of the Qaidam Basin, his study aims to optimize the displacement system and improve the production rate. This paper counters the difficulty of Severely reservoir heterogeneity and interlayer contradictions low producing degree of water drive utilization and unclear understanding of water drive pattern in the study area; the article adopts Reservoir Engineering Methods, applies water injection development displacement and well network optimization evaluation techniques and bases on the geological characteristics of the reservoir to conduct the first time small well spacing pilot test in Block Wu 5, The results confirm that small well spacing encryption is feasible in this block.Therefore, the deployment and adjustment of small well spacing well network encryption in high structural areas of the area. Through implementation in the past two years, The small well spacing encryption in low-permeability sandbar reservoirs can effectively reduce seepage resistance, increase effective driving capacity, improve recovery rate and recovery rate, improve reservoir development efficiency, and increase economic benefits.

A transfer learning framework for the assessment of unconventional resources opportunities in the Middle East

An inadequate understanding of fluid transport processes in ultra-low permeability rocks has propelled data-driven modeling as an alternative and complementary tool to create recovery forecasts that honor the available data in the field. However, newly discovered fields lack data, which makes it challenging to apply traditional data-driven approaches to decision-making. Transfer learning provides an opportunity to start the early-stage analysis of the asset before adequate data becomes available. Here, we present a transfer learning framework that allows the basin-wide assessment of new shale gas and tight oil prospects. The proposed method is developed on real-world data from several thousand horizontal hydraulically fractured wells in the Eagle Ford super-basin in South Texas. A novel aspect of our method is integrating reservoir engineering domain expertise and the relationship among geologic and petrophysical variables, drilling and completion parameters, and well productivity in the data pre-processing and feature generation steps. We consider the temporal and spatial balancing of the training data to ensure that the resulting predictive models honor the real-world practices followed during unconventional field development. Our full-cycle transfer learning workflow consists of dimensionality reduction and unsupervised clustering, supervised learning, and hyperparameter fine-tuning. We test the workflow by examining the performance of a data-driven model of the Eagle Ford Basin on potential plays in the Middle East: Tuwaiq Mountain and Hanifa. The existence of multiple types of hydrocarbons–liquid oil, condensate, and dry gas–in the Eagle Ford results in a model flexible enough to be tested on various types of assets in a new location. We address the challenge of model overfitting by using data types with a relatively low resolution, which allows generalization to different geologies with basin-wide accuracy.