Actual Reservoir Research Articles

Three-Dimensional Geological Modeling Study of the Chang 6 Member in Block A Ordos Basin

Accurate characterization of three-dimensional heterogeneity in tight sandstone oil and gas reservoirs has become a critical research topic for enhancing recovery efficiency. Through core observations and thin-section identification techniques, the lithological characteristics, sedimentary facies distribution patterns, and physical property parameter distribution features of the Chang 6 member in Area A of the Ordos Basin were studied in detail. Utilizing prior research findings, a three-dimensional geological model of the reservoir in Area A was established. The results indicate that: the rock type of the Chang 6 member in Area A is feldspathic sandstone, and the sandbodies within the subaqueous distributary channels are well-developed, with large thickness, wide distribution, and relatively continuous sandbodies; the three-dimensional geological model of the Chang 6 member in Area A shows high consistency with the actual reservoir physical properties, accurately delineating the NE-SW-oriented continuous distribution pattern of subaqueous distributary channel sandbodies. The established three-dimensional geological model provides a robust foundation for reservoir numerical simulation.

X-Ray Computed Tomography for Estimating Density and Porosity in Reservoir Analogous Rocks: A Study on Laminated Limestones from the Crato Formation

In recent decades, faster and more affordable methods for characterizing reservoir rocks in environmental and geological studies have gained importance, particularly for hydrocarbon exploration and resource management. One promising method is X-Ray Computed micro-Tomography (XR-?CT), enabling non-destructive analysis of rock properties. However, this technique presents challenges related to image interpretation, property characterization below the voxel scale, and result comparison across configurations. In this study, laminated limestones from the Crato Formation, analogs to the pre-salt Barra Velha Formation, were analyzed using XR-?CT to estimate density and porosity. These rocks serve as substitutes for actual reservoir conditions, addressing the challenge of limited subsurface samples. This study assessed the feasibility of XR-?CT for characterizing these properties and understanding the impact of millimeter-scale laminations on their distribution. Calibration values for calcite, the primary mineral, were used to ensure accuracy and repeatability. The results demonstrate that XR-?CT is a viable tool for environmental and geological characterization. Laminations due to stratification influenced porosity distribution in the axial direction, with higher concentrations in certain sections of the samples. The porosity values calculated using XR-?CT align relatively well with the gas porosimetry results, with most samples showing a relative difference of less than 10%. However, exceptions were observed in LM4 and T10.2, where the relative difference reached -15.90% and -12.80%, respectively. Despite these challenges, qualitative analysis was achieved. The study highlights the necessity of accounting for mineralogy and calibration in XR-?CT to ensure reliable comparisons across different tomographic systems, enhancing the method’s applicability in environmental and geological studies.

Numerical simulation of shale gas reservoir based on ILU-GMRES method

ABSTRACT The numerical simulation of shale gas reservoirs requires transforming mathematical models into large linear equation systems, and solving large linear equation systems requires a lot of time. A novel ILU-GMRES method for solving large linear equations is presented, which treats the sub matrix discretized at nodes as the basic elements of the coefficient matrix. By combining the incomplete LU decomposition method (ILU) with the generalized minimum residue method (GMRES), the discretized large linear equations are iteratively solved. A comparison was made between the new method and other methods for calculating the actual shale gas reservoir model. The results reveal that the new method has a faster calculation speed in solving linear equation systems. The calculation time of the novel ILU-GMRES method is 1029s, the calculation time of the generalized minimum residual method (GMRES) method is 2709s, the calculation time of the Gauss–Seidel method (GS) is 3786s, and the calculation time of the successive over relaxation method (SOR) method is 3386s, the calculation time of the preprocessing conjugate gradient method (PCG) is 3115s. The calculation speed of the novel method is almost 2.6 times faster than the GMRES method, the novel method is more effective in the numerical simulation of shale gas.

Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network

Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network

An Efficient Method for Identifying Inter-Well Connectivity Using AP Clustering and Graphical Lasso: Validation with Tracer Test Results

Identifying inter-well connectivity is crucial for optimizing reservoir development and facilitating informed adjustments. While current engineering methods are effective, they are often prohibitively expensive due to the complex nature of reservoir conditions. In contrast, methods that utilize historical production data to identify inter-well connectivity offer faster and more cost-effective alternatives. However, when faced with incomplete dynamic data—such as long-term shut-ins and data gaps—these methods may yield substantial errors in correlation results. To address this issue, we have developed an unsupervised machine learning algorithm that integrates sparse inverse covariance estimation with affinity propagation clustering to map and analyze dynamic oil field data. This methodology enables the extraction of inter-well topological structures, facilitating the automatic clustering of producers and the quantitative identification of connectivity between injectors and producers. To mitigate errors associated with sparse production data, our approach employs sparse inverse covariance estimation for preprocessing the production performance data of the wells. This preprocessing step enhances the robustness and accuracy of subsequent clustering and connectivity analyses. The algorithm’s stability and reliability were rigorously evaluated using long-term tracer test results from a test block in an actual reservoir, covering a span of over a decade. The results of the algorithm were compared with those of the tracer test to evaluate its accuracy, precision rate, recall rate, and correlation. The clustering results indicate that wells with similar characteristics and production systems are automatically grouped into distinct clusters, reflecting the underlying geological understanding. The algorithm successfully divided the test block into four macro-regions, consistent with geological interpretations. Furthermore, the algorithm effectively identified the inter-well connectivity between injectors and producers, with connectivity magnitudes aligning closely with actual tracer test data. Overall, the algorithm achieved a precision rate of 79.17%, a recall rate of 90.48%, and an accuracy of 91.07%. This congruence validates the algorithm’s effectiveness in the quantitative analysis of inter-well connectivity and demonstrates significant potential for enhancing the accuracy and efficiency of inter-well connectivity identification.

Hydrogen generated by electrochemical water splitting as electron donor for nitrate removal from micro-polluted reservoir water

Hydrogen generated by electrochemical water splitting as electron donor for nitrate removal from micro-polluted reservoir water

The reservoir screening standard of CO2 huff-n-puff based on orthogonal analysis method and random forest algorithm

In heavy oil reservoirs, favorable reservoir properties have a positive impact on the production performance during CO2 huff-n-puff. It is significant to study the screening method of applicable conditions for CO2 huff-n-puff in actual reservoirs. To solve these problems, this paper introduced the orthogonal design method to analyze the main factors based on numerical simulation. The technical analysis and the economic evaluation were both employed to obtain the applicable conditions of selecting oil layers or injection wells during CO2 huff-n-puff. And a new algorithms of machine learning, the random forest algorithm, was introduce to find the weighted factors and the scoring standards that were suitable for CO2 huff-n-puff. Finally, a set of method for screening suitable reservoir conditions was established. Based on the introduction of orthogonal analysis method and random forest algorithm, a software was established to achieve the purpose of analyzing the feasibility of CO2 huff-n-puff considering different reservoir geological parameters. This method increased the accuracy and efficiency in screening reservoir conditions that was suitable for CO2 huff-n-puff.

Mitigating thermal stratification in lakes/reservoirs through wind-powered air diffusers.

Thermal stratification can cause various water quality issues in large water bodies. To address this, a new wind-powered artificial mixing system is designed and experimentally tested for various Savonius rotor combinations (three-stage and four-stage rotors). These turbines directly utilize wind energy to draw air into the water column for aeration, bypassing the need for electrical conversion. The rotor performances were tested in terms of power and torque coefficients. Additionally, these rotors were tested for artificial mixing efficiencies in a specially designed water tank that can mimic thermal stratification typically observed in an actual water supply reservoir. Among the rotors, the three-stage rotor with a 60° phase shift was found to exhibit superior power and torque coefficients, achieving a power efficiency value of 0.14. As for the mixing efficiency, the four-stage rotor with a 45° phase shift excelled in mixing efficiency, reaching 95%. PRACTITIONER POINTS: A new wind-powered artificial mixing system is designed and tested for various Savonius rotor combinations. While keeping the total rotor height constant, the three-stage Savonius rotor class shows superior performance against the four-stage Savonius rotor class in terms of power and torque efficiency. Apart from the rotor performance results, the four-stage Savonius rotors show greater artificial mixing efficiency than the three-stage Savonius rotors. Single-pump/diffuser artificial destratification system exhibits better mixing efficiency than multiple-pump/diffuser systems.

Towards in-depth profile control using s-MPG synergy with MSRG in fractured-vuggy carbonate reservoirs

Towards in-depth profile control using s-MPG synergy with MSRG in fractured-vuggy carbonate reservoirs

Research on Inter-Fracture Gas Flooding for Horizontal Wells in Changqing Yuan 284 Tight Oil Reservoir

In tight reservoir development, traditional enhanced oil recovery (EOR) methods are incapable of effectively improving oil recovery in tight reservoirs. Given this, inter-fracture flooding is proposed as a new EOR method, and physical model simulation and numerical simulation are performed for inter-fracture water flooding. Compared with inter-fracture water flooding, inter-fracture gas flooding has a higher application prospect. However, few studies on inter-fracture gas flooding have been reported, and its EOR mechanisms and performance are unclear. This paper used the geological model of the actual tight reservoir to carry out numerical simulations for two horizontal wells in the Changqing Yuan 284 block. The results showed that (1) inter-fracture gas flooding can effectively supplement formation energy and increase formation pressure; (2) inter-fracture gas flooding delivers simultaneous displacement, which can effectively increase the swept area in tight reservoirs; (3) injected CO2 dissolves into the reservoir fluid, reduces fluid viscosity, and improves fluid flow through the reservoir; and (4) the recovery factor increment of the CO2 injection is higher than those of natural gas injection and N2 injection. The findings of this research provide references for the production and development of tight reservoirs.

A new approach for fracture parameters optimization of multi-fractured horizontal wells: Virtual boundary method

A new approach for fracture parameters optimization of multi-fractured horizontal wells: Virtual boundary method

Understanding the phase behavior during CO2 flooding by dissipative particle dynamics

Understanding the phase behavior during CO2 flooding by dissipative particle dynamics

Numerical simulation of residual oil distribution characteristic of carbonate reservoir after water flooding

Carbonate reservoirs are characterized by abundant reserves and are currently focal points for development in oil and gas producing regions such as the Ahdab oilfield, Tarim Basin, Sichuan Basin, and Ordos Basin. The primary method for exploiting carbonate reservoirs is waterflooding. However, due to the complex pore structure and pronounced heterogeneity of carbonate rocks, the waterflooding process often leads to an unclear distribution of remaining oil and low waterflooding recovery efficiency, significantly impacting the stable and high production of carbonate reservoirs. This paper presents a two-phase flow model of oil and water in distinct pore structures by integrating fluid flow equations and interface tracking equations. It visually represents the waterflooding process at the pore scale, elucidates the distribution and formation mechanism of remaining oil, and discusses the mechanism of microscopic displacement efficiency change. The study reveals that: 1) After waterflooding, the distribution patterns of remaining oil can be categorized into dead-end remaining oil, pressure balance remaining oil, wall-bound remaining oil, Jamin effect remaining oil, and water-encapsulating remaining oil, which are governed by microscopic pore structure, wettability, and preferential flow paths; 2) From the perspective of actual reservoir displacement efficiency, intergranular pores > intergranular dissolved pores > visceral foramen > mould pore, with this trend being more pronounced under hydrophilic wetting conditions; 3) Given the oil-wet to strong oil-wet wettability characteristics of these carbonate rocks, capillary forces pose significant resistance during waterflooding. The conclusion underscores the importance of leveraging the reservoir’s microscopic pore structure and wettability characteristics for actual oil wells, elucidating the evolutionary law of the mechanical mechanism of oil-water interface advancement, clarifying oil-water percolation characteristics at the pore scale, and understanding the microscopic displacement physical mechanism, all of which are crucial for guiding the design of schemes aimed at enhancing reservoir recovery efficiency.

Temperature transient analysis: Static formation temperature test (SFTT) an overview

Temperature transient analysis: Static formation temperature test (SFTT) an overview

Constraint programming for reservoir operation optimization of Bhumibol dam

The modern constraint programming (CP) was adopted to minimize water scarcity and excessive water which are the critical issues in reservoir operation of Bhumibol Dam (BB) to solve consecutive droughts in the Chao Phraya River Basin (CPYRB), Thailand. The situations of the severe droughts have been frequently occurred in a broad area of CPYRB due to an extremely low rainfall leading to unusually low water levels and inflow in major reservoirs. Therefore, multi-objective optimization was conducted to characterize the actual operation and physical reservoir system of BB Dam. Two different CP models with seasonal and yearly constraints were manipulated using MiniZinc programming language and the constraint solver IPOPT to find the optimal daily release scheme from 2000 to 2018 of BB Dam. The potential of downstream flow conditions was also considered to partially supply downstream water demand and store savable water in a reservoir for subsequent use during possible future droughts. The results reveal that CP models can diminish some extent of yearly reservoir release, while daily long-term release scheme conforms well with the actual operation particularly during dry and wet seasons in specific drought years. These mean that amount of reservoir water of approximately 47.12–103.83 MCM/year can be saved and stored in reservoir for subsequent use in CPYRB when CP models are deployed. This results in a reservoir storage increase of 7.10–7.94% at the end of the wet season for seasonal and yearly CP models, respectively. When potential side flow is considered, the increase climbs up to 10.49%. This envisages the higher possibility of supplying reservoir water for agricultural water needs over the dry season in the Greater Chao Phraya Irrigation Scheme. As the potential hydraulic head is subject to increased reservoir water storage, therefore, the increase in hydropower production is definitely found of ranging from + 6.10% to + 13.79% by these two sorts of CP models. In addition, handling with huge volumes of flood and drought by CP models is well managed as the specific release constraints during refilled and drawdown periods are used to define the constraint satisfaction problem.

Enhancing Oil–Water Flow Prediction in Heterogeneous Porous Media Using Machine Learning

The rapid and accurate forecasting of two-phase flow in porous media is a critical challenge in oil field development, exerting a substantial impact on optimization and decision-making processes. Although the Convolutional Long Short-Term Memory (ConvLSTM) network effectively captures spatiotemporal dynamics, its generalization in predicting complex engineering problems remains limited. Similarly, although the Fourier Neural Operator (FNO) demonstrates adeptness at learning operators for solving partial differential equations (PDEs), it struggles with three-dimensional, long-term prediction. In response to these limitations, we introduce an innovative hybrid model, the Convolutional Long Short-Term Memory-Fourier Neural Operator (CL-FNO), specifically designed for the long-term prediction of three-dimensional two-phase flows. This model integrates a 3D convolutional encoder–decoder structure to extract and generate hierarchical spatial features of the flow fields. It incorporates physical constraints to enhance the model’s forecasts with robustness through the infusion of prior knowledge. Additionally, a temporal function, constructed using gated memory-forgetting mechanisms, augments the model’s capacity to analyze time series data. The efficacy and practicality of the CL-FNO model are validated using a synthetic three-dimensional case study and application to an actual reservoir model.

A prediction model for water absorption in sublayers based on stacking ensemble learning method

A prediction model for water absorption in sublayers based on stacking ensemble learning method

A study on the division and determination of characteristic indexes of underground reservoirs under inclined bottom plates

ABSTRACT In recent years, the slope of the bottom plate of the underground reservoir in the loose porous medium constructed in the middle and upper reaches of the river is relatively large, which makes the underground water surface incline to a certain slope as a whole, near-horizontal section will appear in front of the underground dam during continuous recharge. Given this situation, the definition and calculation of the characteristic index of the underground reservoir in line with the actual changes are carried out. The characteristic water level of the underground reservoir is defined based on the groundwater surface with a certain slope at the inclined level in the case of the lower dam (single dam) impoundment. The characteristic capacity is calculated through the water level at the typical section defined by the inclined surface. A new set of definition and calculation method based on the characteristic index of the underground reservoir with a dam on the inclined bottom plates is proposed and applied to the Rushan River underground reservoir, which more accurately reflects the water level change and the corresponding actual reservoir capacity during the operation of the underground reservoir.

Performance investigations of auxiliary‐space Maxwell solver preconditioned iterative algorithm for controlled‐source electromagnetic induction problems with electrical anisotropy

AbstractMany studies have highlighted the superior performance of iterative solvers employing the auxiliary‐space Maxwell solver preconditioner in controlled‐source electromagnetic induction problems featuring isotropic conductivity. The importance of considering the presence of electrical anisotropy in controlled‐source electromagnetic data has been well recognized. However, considering anisotropic conductivity will impose difficulty in robustly solving the final system of linear equations as the electrical anisotropy may significantly increase its condition number and degrade the performances of iterative solvers. Whether or not iterative solvers using the auxiliary‐space Maxwell solver preconditioner have similar superior performances in the case of arbitrary electrical anisotropy is still an issue to be discussed. In this study, within the framework of finite element simulation employing unstructured tetrahedral meshes, we conduct a comprehensive examination to evaluate the performance of the flexible generalized minimum residual solver with the auxiliary‐space Maxwell solver preconditioner for three‐dimensional controlled‐source electromagnetic forward modelling problems involving arbitrary anisotropic media. Tests on synthetic one‐ and three‐dimensional models show that our iterative scheme performs better than widely used iterative or direct solvers for controlled‐source electromagnetic anisotropy forward problems. Its convergence rate is nearly independent of working frequencies, anisotropy ratio and problem size. Finally, we applied the newly developed parallel iterative scheme to the Bay du Nord reservoir in a complicated real‐life offshore hydrocarbon exploration scenario characterized by anisotropic conductivity, in which our iterative scheme with an auxiliary‐space Maxwell solver preconditioner has good robustness. Furthermore, we investigated how data responses at different frequencies are sensitive to the actual hydrocarbon reservoir. Our sensitivity analysis revealed that data at large measuring offsets are considerably more sensitive to the reservoir than data at shorter measuring offsets. We also assessed the impact of neglecting anisotropy in data analysis for the realistic example and found that ignoring anisotropy can lead to noticeable changes in the data. This suggests that considering anisotropy in the interpretation of the observed data is essential to guarantee the precision of controlled‐source electromagnetic field surveys.

Studies into confined methane adsorption in shale nanopores using a simplified local density model

Studies into confined methane adsorption in shale nanopores using a simplified local density model