Search Results (29)

Carbonate fracture–cavity reservoirs are significant oil and gas reservoirs globally, and their efficient development is influenced by the connectivity between fracture–cavity units within the reservoir. These reservoirs primarily consist of large caves, dissolution holes, and natural fractures, which serve as the primary storage and flow spaces. The S91 unit of the Tarim Oilfield is a karstic fracture–cavity reservoir with shallow coverage. It exhibits significant heterogeneity in the fracture–cavity reservoirs and presents complex connectivity between the fracture–cavity bodies. The integration of static and dynamic data, including geology, well logging, seismic, and production dynamics, resulted in the development of a set of static and dynamic connectivity evaluation processes designed for highly heterogeneous fracture–cavity reservoirs. Methods include using structural gradient tensors and stratigraphic continuity attributes to delineate the boundaries of caves and holes; performing RGB fusion analysis of coherence, curvature, and variance attributes to characterize large-scale fault development features; applying ant-tracking algorithms and fracture simulation techniques to identify the distribution and density characteristics of fracture zones; utilizing 3D visualization technology to describe the spatial relationship between fracture–cavity units and large-scale faults and fracture development zones; and combining dynamic data to verify interwell connectivity. This process will provide a key geological basis for optimizing well network deployment, improving water and gas injection efficiency, predicting residual oil distribution, and formulating adjustment measures, thereby improving the development efficiency of such complex reservoirs. Full article

Supercritical carbon dioxide (Sc-CO₂) thermal shock fracturing emerges as an innovative rock fragmentation technology combining environmental sustainability with operational efficiency. This study establishes a thermo-hydro-mechanical coupled model to elucidate how in situ stress magnitude and anisotropy critically govern damage progression and fluid dynamics during Sc-CO₂ thermal shock fracturing. Key novel findings reveal the following: (1) The fracturing mechanism integrates transient hydrodynamic shock with quasi-static pressure loading, generating characteristic bimodal pressure curves where secondary peak amplification specifically indicates inhibited interwell fracture coalescence under anisotropic stress configurations. (2) Fracture paths undergo spatiotemporal reorientation—initial propagation aligns with in situ stress orientation, while subsequent growth follows thermal shock-induced principal stress trajectories. (3) Stress heterogeneity modulates fracture network complexity through confinement effects: elevated normal stresses perpendicular to fracture planes reduce pressure gradients (compared to isotropic conditions) and delay crack initiation, yet sustain higher pressure plateaus by constraining fracture connectivity despite fluid leakage. Numerical simulations systematically demonstrate that stress anisotropy plays a dual role—enhancing peak pressures while limiting fracture network development. This demonstrates the dual roles of the technology in enhancing environmental sustainability through waterless operations and reducing carbon footprint. Full article

The steam flooding–gravity drainage technology has become one of the effective alternative development methods in the middle and later stages of thin-layer ultra-viscous oil steam throughput, with predicted recovery rate of over 50%. Currently, there is a lack of relevant technical research on the composite swallowing and spitting preheating stage. This is in response to the slow preheating of the oilfield and the large differences in connectivity between injection and production wells. The dynamic analysis method was used to analyze the key factors that restrict the efficient connectivity of steam throughput preheating. Based on this, a series steam throughput preheating efficient connectivity technologies were proposed. Physical simulation, numerical simulation, and other methods were used to characterize and demonstrate the technical principles and operating of the efficient connectivity technology. The research results were successfully applied to the super-viscous oil reservoirs of the Fengcheng oilfield in Xinjiang. The results show that the main factors severely limiting the balanced and rapid connectivity between injection and production wells are the limited radius of steam coverage, low utilization degree oil layers, and frequent unilateral steam breakthroughs. The reservoir expansion transformation has improved the reservoir properties along the horizontal section, increasing the utilization rate of the horizontal section from 51% to 90%, achieving rapid connectivity injection and production wells, and shortening the conventional throughput preheating cycle by 3–4 cycles. The group combination steam injection method achieved a centralized increase in thermal energy, with the inter-well connectivity changing from unidirectional to a broader area The reasonable steam injection intensity was 15 t/m, the regional temperature field increased from 83 °C to 112 °C, and the steam area expanded by approximately 10 m. The multi-medium composite technology achieved a dual increase in steam coverage and profile utilization, with the steam coverage radius increasing by 15 m and the oil reservoir profile utilization increasing by more than 30%. The temporary plugging and fracturing of the reservoir achieved the sealing of inherited breakthrough channels, directing the steam to unused areas, increasing the utilization rate to 89.2%, and shortening the throughput preheating cycle by 3 cycles. This series of technologies has achieved remarkable results in actual application in super-heavy oilfield, which has certain reference significance for the efficient and low-carbon development of heavy oil steam throughput reservoir turning into drive and release. Full article

The development of inter-well channeling pathways has become a major challenge restricting the effectiveness of the thermal recovery process for heavy oil reservoirs, which leads to non-uniform sweep and reduced oil recovery. This is especially true for the characteristics of the higher injection–production intensity in offshore operations, making the issue more prominent. In this study, a quick and widely applicable approach is proposed for channeling identification, utilizing the static reservoir parameters and injection–production performance. The results show that the cumulative injection–production pressure differential (CIPPD) over the cumulative water equivalent (CWE) exhibits a linear relationship when connectivity exists between the injection and production wells. Thereafter, the seepage resistance could be analyzed quantitatively by the slope of the linear relationship during the steam injection process. Simultaneously, a channeling identification chart could be obtained based on the data of injection–production performance, dividing the steam flooding process into three different stages, including the energy recharge zone, interference zone, and channeling zone. Then, the established channeling identification chart is applied to injection–production data from two typical wells in the Bohai oilfield. From the obtained channeling identification chart, it is shown that Well X1 exhibits no channeling, while Well X2 exhibited channeling in the late stage of the steam flooding process. These findings are validated against the field performance (i.e., the liquid rate, water cut, flowing temperature, and flowing pressure) to confirm the accuracy. The channeling identification approach in this paper provides a guide for operational adjustments to improve the effect of the thermal recovery process in the field. Full article

Understanding interwell connectivity during water-flooding reservoir development is crucial for analyzing the characteristics of remaining oil and optimizing technical measures. The key lies in establishing an inversion method to identify interwell connectivity. However, traditional history matching methods based on numerical simulation suffer from high computational costs and limited adaptability to complex spatiotemporal dependencies in production data. To address these challenges, this study combines a surrogate model trained using a graph neural network (GNN) and Transformer encoder with a differential evolution particle swarm optimization (DEPSO) algorithm for automated reservoir history matching. The surrogate model is constructed by embedding the capacitance–resistance model (CRM) into a graph structure, where wells are represented as nodes and interwell connectivity parameters as edge features. When applied to the conceptual model, the coefficient of determination (R²) was found to be approximately 0.95 during the training phase by comparing the production data predicted by the surrogate model with the actual observed data. The DEPSO algorithm aimed to minimize the differences between surrogate predictions and observed data, achieving good fitting results. When applied to a complex case study, the average water-cut fitting rate for each production well in its well group reached 87.8%. The results indicate that this method significantly improves fitting accuracy and has substantial practical value. Full article

Reservoir management becomes increasingly critical as fields decline to a fully mature state. During this stage, engineers and managers must make decisions based on a limited set of field measurements (such as pressure and production rates). At the same time, up-to-date information concerning the reservoir’s geophysical characteristics and petrochemical properties may be unavailable. To aid in the expert’s appraisal of this production scenario, we present the results of applying a data-driven methodology based on visibility graph analysis (VGA) and multiplex visibility graphs (MVGs). It infers inter-well connectivities at the reservoir level and clarifies the degrees of mutual influence among wells. This parameter-free technique supersedes the limitations of traditional methods, such as the capacitance–resistance (CR) models and inter-well numerical simulation models (INSIMs) that rely heavily on geophysical data and are sensitive to porous datasets. We tested the method with actual data representing a field’s state over 62 years. The technique revealed short- and long-term dependencies between wells when applied to historical records of production rates (oil, water, and gas) and pressures (bottom and wellhead). The inferred connectivity aligned with documented operational trends and successfully identified stable connectivity structures. In addition, the interlayer mutual information (IMI) parameter exceeded 0.75 in most periods, confirming high temporal consistency. Moreover, validation by field experts confirmed that the inferred interconnectivity was consistent with the observed production. Full article

In this paper, aiming at the challenges of injection–production optimization, especially the contradiction between injection and production in water flooding development of oil and gas fields in China, an interwell connectivity analysis method (TAGNN) based on the time–space scale coupling of injection–production data is proposed. This method uses the existing injection–production well data, combined with the reservoir system seepage mechanics law, to quantitatively characterize and evaluate the interwell connectivity, which overcomes the limitations of traditional methods. The TAGNN method introduces asymmetric time alignment and advanced feature extraction technology to solve the problem of asymmetric injection–production data in time dimension, and considers the spatio-temporal scale coupling characteristics of injection–production data, which can capture the temporal variation and spatial distribution characteristics of data at the same time. The experimental results showed that this method more accurately reflected the interwell connectivity status and improved the fitting and prediction accuracy, compared with the existing GNN method. This method can promote the effective injection of water from the injection well to the production well and optimize the injection production structure and development plan, thereby improving the recovery rate. Full article

Given that more and more oil reservoirs are reaching the high water cut stage during water flooding, the construction of an advanced algorithmic model for identifying inter-well connectivity is crucial to improve oil recovery and extend the oilfield service life cycle. This study proposes a state variable-based dynamic capacitance (SV-DC) model that integrates artificial intelligence techniques with dynamic data and geological features to more accurately identify inter-well connectivity and its evolution. A comprehensive sensitivity analysis was performed on single-well pairs and multi-well groups regarding the permeability amplitude, the width of the high permeable channel, change, and lasting period of injection pressure. In addition, the production performance of multi-well groups, especially the development of ineffective circulation channels and their effects on reservoir development, are studied in-depth. The results show that higher permeability, wider permeable channels, and longer injection pressure maintenance can significantly enhance inter-well connectivity coefficients and reduce time-lag coefficients. Inter-well connectivity in multi-well systems is significantly affected by well-group configuration and inter-well interference effects. Based on the simulation results, the evaluation index of ineffective circulation channels is proposed and applied to dozens of well groups. These identified ineffective circulation channel changing patterns provide an important basis for optimizing oil fields’ injection and production strategies through data-driven insights and contribute to improving oil recovery. The integration of artificial intelligence enhances the ability to analyze complex datasets, allowing for more precise adjustments in field operations. This paper’s research ideas and findings can be confidently extended to other engineering scenarios, such as geothermal development and carbon dioxide storage, where AI-based models can further refine and optimize resource management and operational strategies. Full article

Offshore oilfields are characterized by loose sandstone reservoirs, strong heterogeneity and high injection and production intensity. Water channeling gradually develops after entering the high water cut stage, which weakens production performance. Current identification methods usually have high computational costs and low efficiency. A quantitative identification model of water channeling based on inter-well connection units has been established by simplifying the complex reservoir system into a connection network between injectors and producers, which can quickly and accurately obtain strength characteristic parameters for waterflow channels. In addition, a comprehensive evaluation factor M and classification standard for water channeling suitable for offshore heterogeneous reservoirs have been proposed. It indicates a thief zone when M is larger than 0.65, a predominant waterflow channel when M is between 0.55 and 0.65, and no water channeling when M is smaller than 0.55. The application of (an) offshore S oilfield demonstrates that the new method successfully identifies 18 segments of the thief zone and 19 segments of the predominant waterflow channel and improves computational speed by 100 times compared with the conventional numerical modeling method. This novel method allows for rapid and accurate identification and prediction of water channeling, including location, directions, and strengths, thereby providing timely and practical guidance for inefficient water channel treatment. Full article

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. Full article

At present, many offshore oil fields are facing problems, such as pollution-induced near-well zone blockage, poor inter-well connectivity, and strong vertical heterogeneity, which lead to insufficient formation energy and low production in the middle and late stages of development. It is necessary to develop a new technology to overcome these issues. In this regard, water-injection-induced dilation technology, which was already proven to have positive effects on loose sandstone reservoirs, was controversially applied to an offshore low-permeability reservoir. To investigate whether the water-injection-induced dilation technology is suitable, experiments were conducted to analyze the dilation potential of offshore low-permeability sandstone reservoirs, namely, X-ray diffraction, laser particle size analysis, physical simulation, computed tomography scan, and electron microscope scanning experiments. The X-ray diffraction experiments showed that the samples had more than 80% non-clay mineral content and a high brittleness index, which meant more complex microfractures under water injection. Particle size analysis experiments revealed that the particle size was mainly between 10 μm and 100 μm, and thus belonged to coarse silty sand. According to the sorting grade, the sample particle size distribution was uniform and the reservoir was more prone to dilation. The true triaxial physical simulation showed that a volumetric dilation zone occurred around the wellbore, where complicated microfractures occurred. This paper provides adequate evidence and mechanisms of dilation potential for an offshore low-permeability sandstone reservoir. Full article

In response to the fracturing disturbance problem in the Mahu 18 block, based on the production data of on-site fracturing disturbance well groups, the actual fracturing disturbance cases in the block were first statistically divided. The complex fracturing disturbance situation in the block was divided according to the relationship between the number of fracturing wells and the number of production wells. Then, the disturbance types were classified based on the production dynamic response characteristics of the disturbed wells, and the degree of disturbance was quantitatively evaluated for the different types. The results indicate two main types of fracturing interference in the Mahu 18 block: the fracture communication type, and the pressure wave interference type formed through inter-well connectivity through the reservoir matrix. The fracturing disturbance dominated by fracture communication can cause serious water channeling to the production well through the direct connection of inter-well fractures, leading to a surge in water production. This type of fracturing disturbance often has a severe negative impact on the production well. In addition, the pressure and production (water production, liquid production, oil production) in the production dynamic response characteristics of disturbed wells during fracturing disturbance were used as evaluation indicators to quantify the impact of different types of fracturing disturbance from multiple perspectives. Full article

This study aimed to develop and implement a nanotechnology-based alternative to traditional tracers used in the oil and gas industry for assessing interwell connectivity. A simple and rapid hydrothermal protocol for synthesizing carbon quantum dots (CQDs) using agroindustry waste was implemented. Three commercial CQDs were employed (CQDblue, CQDgreen, and CQDred); the fourth was synthesized from orange peel (CQDop). The CQDs from waste and other commercials with spherical morphology, nanometric sizes less than 11 nm in diameter, and surface roughness less than 3.1 nm were used. These tracers demonstrated high colloidal stability with a negative zeta potential, containing carbonyl-type chemical groups and unsaturations in aromatic structures that influenced their optical behavior. All materials presented high colloidal stability with negative values of charge z potential between −17.8 and −49.1. Additionally, individual quantification of these tracers is feasible even in scenarios where multiple CQDs are present in the effluent with a maximum percentage of interference of 15.5% for CQDop in the presence of the other three nanotracers. The CQDs were injected into the field once the technology was insured under laboratory conditions. Monitoring the effluents allowed the determination of connectivity for five first-line producer wells. This study enables the application of CQDs in the industry, particularly in fields where the arrangement of injector and producer wells is intricate, requiring the use of multiple tracers for a comprehensive description of the system. Full article

The D reservoir in the Bongor Basin, southern Chad, is highly heterogeneous. In the stage of waterflood development, the injected water is seriously channeled along the dominant channel, and the water drive effect becomes worse. At the same time, due to the strong edge and bottom water, the water flooding situation is aggravated, the water cut is increased, and the development efficiency is reduced. To accurately identify the inter-well connectivity relationship, we developed a reservoir inter-well connectivity model based on the principle of inter-well connectivity and dynamic production data and reservoir geological parameters. Thus, the plane water injection split coefficient and water injection efficiency of each reservoir layer were obtained. The results are in good agreement with the calculation results of inter-well connectivity through verification with field tracer interpretation. The practical application results show that the method can increase the annual output of oil by 1.3%, which has a good oil increase effect. In this study, a model of inter-well connectivity in multi-layer sandstone reservoirs was established for the first time. The production performance of the model injection–production well was optimized in real time by a historical fitting and production optimization algorithm and then applied to real reservoirs, so that it could effectively improve the oilfield development and optimize the injection–production structure. Full article

The existence of the bottom oil–water transition zone (BTZ) greatly impairs the performance of the conventional steam-assisted gravity drainage (SAGD) process and its mitigation measures are very limited. In order to accelerate oil production and decrease the Steam-to-Oil Ratio (SOR), a promising technology involving a steam drive and gravity drainage (SDGD) process by placing dual-horizontal wells with high permeability in the BTZ was systematically studied. This paper conducted two-dimensional (2D) and three-dimensional (3D) physical simulations as well as 2D numerical simulation of the SDGD process to explore the mechanism, potential, and application conditions. The research findings indicate that the SDGD process in the BTZ with enhanced permeability through dilation stimulation can achieve higher oil production and lower SOR than the SAGD process. This process fully leverages the advantage of the BTZ to quickly establish inter-well thermal and hydraulic connectivity. The steam chamber first forms around the injector and then spreads towards the producer. By exerting the horizontal displacement of drained oil, oil production rapidly ramps up and keeps at a high rate under the synergistic effect of steam drive and gravity drainage. These insights enhance our understanding of the mechanism, potential, and application conditions of the SDGD process in the confined BTZ to develop super heavy oil or oil sands. Full article