Simple Reservoir Research Articles

Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks

The oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable clusters in complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heterogeneous geological settings, traditional statistical methods seldom provide correct results. Therefore, this paper introduces a robust unsupervised AI strategy designed to identify and classify profitable zones using self-organizing maps (SOM) and K-means clustering techniques. Results of SOM and K-means clustering provided the reservoir potentials of six depositional facies types (MBSD, DCSD, MBSMD, SSiCL, SMDFM, MBSh) based on cluster distributions. The depositional facies MBSD and DCSD exhibited high similarity and achieved a maximum effective porosity (PHIE) value of ≥ 15%, indicating good reservoir rock typing (RRT) features. The density-based spatial clustering of applications with noise (DBSCAN) showed minimum outliers through meta cluster attributes and confirmed the reliability of the generated cluster results. Shapley Additive Explanations (SHAP) model identified PHIE as the most significant parameter and was beneficial in identifying payable and non-payable clustering zones. Additionally, this strategy highlights the importance of unsupervised AI in managing profitable cluster distribution across various geological formations, going beyond simple reservoir characterization.

Streamlined integrated protein isoelectric focusing using microfluidic paper-based device

An innovative integrated paper-based microdevice was developed for protein separation by isoelectric focusing (IEF), allowing for robust design thanks to a 3D-printed holder integrating separation channel, reservoirs, and electrodes. To reach robustness and precision, the optimization focused on the holder geometry, the paper nature, the reservoir design, the IEF medium, and various focusing parameters. A well-established and stable pH gradient was obtained on a glass-fiber paper substrate with simple sponge reservoirs, and the integration of the electrodes in the holder led to a straightforward system. The separation medium composed of water/glycerol (85/15, v/v) allowed for reducing medium evaporation while being an efficient medium for most hydrophobic and hydrophilic proteins, compatible with mass spectrometry detection for further proteomics developments. To our knowledge, this is the first report of the use of glycerol solutions as a separation medium in a paper-based microdevice. Analytical performances regarding pH gradient generation, pI determination, separation efficiency, and resolution were estimated while varying the IEF experimental parameters. The overall process led to an efficient separation within 25 min. Then, this methodology was applied to a sample composed of saliva doped with proteins. A minimal matrix effect was evidenced, underscoring the practical viability of our platform. This low-cost, versatile and robust paper-based IEF microdevice opens the way to various applications, ranging from sample pre-treatment to integration in an overall proteomic-on-a-chip device.

Application of latent variable evolution for channel reservoir characterization using generative adversarial networks and particle swarm optimization

When characterizing channel reservoirs, ensemble-based methods may compromise channel features due to the conflict between the Gaussian distribution assumption of the methods and bimodality of channel reservoirs. To overcome this challenge, one effective approach involves leveraging generative models. Specifically, a generative model trained with channel reservoir models has ability to generate bimodally distributed values from latent vectors whose elements are intentionally designed to conform a Gaussian distribution. Therefore, it resolves the conflict, allowing the ensemble-based methods to update the latent vectors.However, generating initial latent vectors randomly poses an additional challenge and can lead to a suboptimal quality of the initial ensemble. They can significantly influence the unreliability of characterizations, primarily because their impact is strongly tied to the quality of the initial models in the ensemble-based methods.To address this challenge, we propose a novel scheme for channel reservoir characterization using Latent Variable Evolution. In this study, we adopt Generative Adversarial Networks (GAN) and Particle Swarm Optimization (PSO). PSO explores the latent space of GAN trained with channel reservoirs in order to find ones that generate models resembling a true reservoir. Validation on simple and complex reservoir cases demonstrates our proposed method can efficiently provide reliable channel reservoir characterization regardless of the quality of the initial latent vectors. These results are compared to Ensemble Smoother with Multiple Data Assimilation with GAN.

An efficient transformer-based surrogate model with end-to-end training strategies for automatic history matching

In recent years, surrogate models have gained popularity as a tool to tackle the challenges presented by time-consuming numerical simulations in automatic history matching (AHM). Although there are many different surrogate models designed to alleviate this issue, it is still challenging for most of them to handle the high dimensionality and strong non-linearity in reservoir models and dynamic production data. Inspired by the rapid development of Transformer, we propose a novel hybrid hierarchical Vision Transformer (HHVT) approach for history matching, which utilizes a unified architecture to predict production data for specific physical fields with an end-to-end strategy. For predicting the production data of wells, the Transformer supports parallelism computation among multiple timesteps, which shows more superiority than traditional recurrent neural networks. Specifically, our approach constructs a novel encoder-decoder Transformer architecture to learn the implicit features of high-level spatial parameters to match the features of time-series production data. With this architecture, HHVT achieves fast training and inference, which is suitable for large-scale datasets and high-dimensional features. The proposed HHVT model is integrated with a multimodal optimization algorithm to find history-matching solutions. We first validated the effect of hyperparameters of HHVT on a simple 2D reservoir. Moreover, the proposed method was verified on the complex 3D Brugge model. The results demonstrate that the training speed of the Transformer-based model is approximately twice as fast as the surrogates based on convolutional and recurrent neural networks. The proposed HHVT also shows better prediction accuracy in two cases, compared with other surrogate models, which enhances the applicability of surrogate-based history-matching methods in large-scale complex reservoir scenarios.

Reservoir Sampling over Joins

Sampling over joins is a fundamental task in large-scale data analytics. Instead of computing the full join results, which could be massive, a uniform sample of the join results would suffice for many purposes, such as answering analytical queries or training machine learning models. In this paper, we study the problem of how to maintain a random sample over joins while the tuples are streaming in. Without the join, this problem can be solved by some simple and classical reservoir sampling algorithms. However, the join operator makes the problem significantly harder, as the join size can be polynomially larger than the input. We present a new algorithm for this problem that achieves a near-linear complexity. The key technical components are a generalized reservoir sampling algorithm that supports a predicate, and a dynamic index for sampling over joins. We also conduct extensive experiments on both graph and relational data over various join queries, and the experimental results demonstrate significant performance improvement over the state of the art.

New Insights into Turbulent and Laminar Flow Relationships Using Darcy–Weisbach and Poiseuille Laws

Analytical solutions for turbulent and laminar water flow conditions are developed considering the drainage process of a simple tank reservoir, using the Darcy–Weisbach and the Poiseuille laws, respectively. Near the critical value of the Reynolds number, the Darcy–Weisbach and the Poiseuille laws do not match, and there is a gap regarding the possibility of describing the drainage analytically. This gap corresponds to the critical zone of the Moody diagram, where theoretically a constant discharge occurs under a decreasing hydraulic gradient. In the critical zone, this hydraulic behavior of the flow reflects the different energy losses occurring during the drainage, as the laminar flow is a more efficient flow condition than the turbulent one. In natural systems (e.g., springs and karst aquifers), a smooth transition from the turbulent to the laminar water flow occurs due to the heterogeneity in the medium.

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098–0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Influence of sedimentary architecture on static connectivity and geothermal doublet performance (Mezőberény, SE Hungary)

Although the Pannonian Basin in Hungary boasts significant geothermal potential, most geothermal wells in the region suffer from reinjection issues. The aim of this study is to examine the impact of the sedimentary architecture of a paralic lacustrine succession on the static connectivity of a geothermal aquifer. This problem was examined for a case-study geothermal doublet drilled in Mezőberény, southeast Hungary, which experienced injectivity problems shortly after commencing operations in 2012, ultimately leading to shut-in of the wells. A characterization of the architecture of the deltaic aquifer has been undertaken through a novel workflow that combines sedimentological and sequence stratigraphic analysis of the well-doublet and stochastic 3D modelling techniques to assess the likely static connectivity between the production and injection wells. Planform dimension of aquifer-forming mouth-bar elements and fluvial channel bodies were estimated from geological analogues, selected based on the thickness values of such sedimentary bodies interpreted on the gamma-ray logs of the well doublet and nearby hydrocarbon wells. In total five mouth-bar and two fluvial channel input geometry groups were selected, and fifty object-based realizations were constructed to analyse the static connectivity of different geological scenarios. The internal lithology of mouth-bar and fluvial-channel elements were modelled by sequential indicator simulations (ten realizations). Results of connected volume analysis indicate that the sandstone content of the aquifer is located within vertically segmented fluvial-channel and mouth-bar elements separated by mud-prone prodelta and lower delta-front deposits. This architectural configuration, in combination with low net-sandstone content suggests limited connectivity between the wells. Across the set of stochastic realizations, the volume of sandstone connecting the injection and production wells is ∼4% of the total aquifer volume, on average. Results of this study shows that simplified reservoir models can lead to over-estimation of connectivity of geothermal doublets. The proposed workflow combines sedimentological analysis and 3D modelling supported by geological analogues to predict sandstone reservoir connectivity of deltaic geothermal reservoirs. The described workflow can be carried out in target areas using existing boreholes to assist borehole planning.

Neglecting hydrological errors can severely impact predictions of water resource system performance

Risk-based decision making for water resource systems often relies on streamflow predictions from hydrological models. These predictions are integral for estimating the frequency of high consequence extreme events, such as floods and droughts. However, streamflow predictions are known to have errors due to various factors such as incomplete hydrological understanding, parameter misspecification, and uncertain data. Despite these errors being well known, they are frequently neglected when undertaking risk-based decision-making. This paper demonstrates that neglecting hydrological errors can impact on drought risk estimation for high stakes decisions with potentially severe consequences for water resource system performance. A generic framework is introduced to evaluate the impact of hydrological errors for a wide range of water resource system properties. This framework is applied in two Australian case study catchments, where we use a stochastic rainfall model, the GR4J hydrological model, a residual error model, and a simplified reservoir storage model to estimate water resource performance metrics (risk and yield). The results underscore the impact of neglecting hydrological errors on decision-making. In one case study catchment, the yield was over-estimated by ∼15%–55%, resulting in the (actual) risk of running out of water being ∼2–30 times larger than reservoir design. The magnitude of these errors in water resource performance metrics is striking, especially considering that the streamflow predictions appear reasonable based on typical performance metrics (e.g., NSE of ∼0.7). The errors in performance metrics stem from the complex propagation of hydrological errors through the water resource system modelling chain. By accounting for critically important hydrological errors we can mitigate highly erroneous risk estimates and improve decision-making related to water resource management.

Modelling of CO2 Sequestration in Deep Saline Aquifer Reservoir using Surface Evolver Software

A strategy to mitigate CO2 concentration in the atmosphere is injection of supercritical CO2 in porous sedimentary medium filled with saline water where CO2 gets trapped in the pores. Sequestration is resultant of two processes, drainage and imbibition in accordance with capillary law. Simulation of the pore-scale trapping has been performed on reservoir scale. A simple model reservoir consisting of 1000 tetrahedral subunits was constructed. Using Surface Evolver software, pore volueach subunit. The CO2 pressure and liquid saturation curves of the reservoir displayed hysteretic character, indicating that the evolution of the system is pathway-dependent. The simulated curves illustrate the pathway dependency of the residual gas saturation, an important parameter controlling the amount of CO2 stored in the reservoir, hence the efficiency of the CO2 storage. Our study also shows the existence of critical thresholds in terms of in/out gassing during the drainage or imbibition processes

Transient-based multiple branch detection in reservoir pipeline valve systems

Efficient management of pipeline assets is crucial for a reliable urban water supply. Unknown side branches in pipeline systems can hinder the prediction of hydraulic transients, owing to unexpected pressure responses. Herein, we developed a method for detecting multiple side branches in a simple reservoir pipeline valve system. A polynomial-based transient characterization scheme was proposed to address nonlinear valve manoeuvres, which substantially improved the transient simulations over conventional orifice-based approximations. A procedure for pressure signal manipulation via the superimposition of pressure signals with and without branches and isolation of the interference between various branched elements, which distorted the pressure response of multiple branches, was introduced. The model performance was evaluated by comparing the interference-isolated signals obtained from the conventional method, proposed scheme, and experimental datasets. Effective phase matching of the pressure responses obtained via the proposed scheme and experimental data indicated the potential of the former for detecting multiple branches.

Physics-Aware Degradation Model of Lithium-ion Battery Energy Storage for Techno-Economic Studies in Power Systems

Power system operation and planning decisions for lithium-ion battery energy storage systems are mainly derived using their simplified linear models. While these models are computationally simple, they have limitations in how they estimate battery degradation, either using the energy throughput or the Rainflow method. This paper proposes a hybrid approach for lithium-ion battery system modeling suitable for use in power system studies that enhances representation of battery degradation at a reasonable computational cost. The proposed hybrid model combines a physics-based model for improved degradation estimates with a simple and linear energy reservoir model commonly used to represent a battery storage system. The advantage of constructing the battery model with this vision is that it allows its smooth integration into the mixed-integer optimization frameworks. The proposed model is evaluated using both power- and energy-based use cases from the electrical grid. The simulations demonstrate that degradation could be reduced by 45% compared to other modeling strategies while generating the same level of operation profits. The net result of using the proposed hybrid model could be an extended lifespan through better informed planning and operating decisions for energy storage assets.

Neuromorphic computing with hybrid CNN–Stochastic Reservoir for time series WiFi based human activity recognition

Wi-Fi Channel State Information (CSI) based human activity recognition (HAR) which using channel disturbances caused by signal reflection is a novel way of environment sensing and motion recognition. The collected channels characteristics are heavily influenced by the environment, human activity patterns and subject’s weight and height. These signal variations reflected from body components are mainly affected by static multipath effects comprises random noise and behave differently in individuals, and thus an active field of research. To reach further for achieving automated real-time classification, lower computational cost and easy adaptability to hardware are necessary. In this work, a CSI-based HAR with hybrid framework, Convolutional Neural Network (CNN)-Stochastic Reservoir (SR) (CNN-SR) has been proposed, enabling a subject adaptable and more efficient hardware implementation with minimal computational complexity. A subcarrier correlation matrix is first computed and portrayed in image without preprocessing based on the reflection of the raw CSI signal induced by human activities at regular intervals, allowing visual observation of whole pattern changes. The time-based features are subsequently extracted through CNN and these feature arrays are then feed into SR which based on stochastic spiking neural network (SSNN) in simple cycle reservoir architecture for template matching. SR offers attractive power savings over typical von Neumann systems, by doing stochastic computations. The proposed method has also been demonstrated that is capable for HAR based on partially captured signals. The signal pattern of each segment can be observed in a single sight and then employed for person-to-person template recognition. This enables HAR with minimal computational complexity and solving the inter-person variability concerns. The results demonstrate that the proposed CNN-SR achieves impressive performance in recognizing human activities and surpasses existing models with an average accuracy of 93.49%.

Dimensionless Pressure Response Analysis for Water Supply Pipeline Systems with or without Pumping Station

Dimensionless governing equations of unsteady flow and solutions for the pipeline systems having a surge tank were developed. Frequency domain pressure response expressions for two widely used water supply systems were analyzed in dimensionless frequency and time domains. One is the simple reservoir pipeline surge tank valve system and the other is the pipeline system with pump and check valve protected by surge tank. Two different dimensionless expressions for the surge tank were developed and the performance of two expressions was compared. The frequency response pattern of impedance at the downstream valve indicated that the system resonance was determined by the lengths of the main pipeline and the connector and the locations of the surge tank and check valve for the protection of the pumping station. The difference between the simple pipeline system and that of a pump with a check valve was expressed in terms of the phase difference in frequency response distribution. The integrated pressure response for the protected pipeline section was evaluated considering the impact of surge protection in the frequency domain. A better understanding of system behavior can be obtained in terms of specific component isolation both in the frequency and time domain pressure responses. The driven responses in the frequency and time domain can be an important basis for optimum design and operation conditions of water supply systems in dimensionless space.

Autonomous Inflow Control Valve for Medium-Light Oil and Its Effective Performance

Summary Many horizontal oil wells will after a time start producing unwanted fluids. Autonomous inflow control valves may help to choke these unwanted fluids and consequently improve carbon efficiency. This paper publishes new experimental data describing how an autonomous inflow control valve manages medium-light oil (6 cp), water, and gas at reservoir conditions. A further objective is to evaluate how this valve might impact well performance under various conditions. To verify the single- and multiphase flow behavior of the valve, extensive flow loop experiments were performed. Initial testing was done in a model fluid laboratory, while a more extensive test was performed at reservoir conditions (i.e., with formation water, reservoir oil, and hydrocarbon gas at the given reservoir temperature and pressure). To explore and understand the impact of this valve for various reservoir scenarios, a simple conceptual reservoir model with realistic boundary conditions was used. At various differential pressures, the single-phase oil, water, and gas rates were measured. Performance at varying water and gas fractions was measured to get an improved understanding and knowledge of multiphase flow occurring in a well. The results show clearly that the valve will choke gas and water effectively, both at single-phase and multiphase flow conditions. The reservoir and model fluid evaluations show consistent results. The valve shows roughly a monotonic decreasing total rate with decreasing oil fraction, implying that the valve will always prioritize sections with the largest oil fraction. A mathematical model match of the valve performance is possible via the 10-parameter extended autonomous inflow control device (AICD) equation that enables practical evaluation of the valve in industry-standard reservoir simulators. Various scenarios are explored with a conceptual reservoir model, and the autonomous inflow control valve shows its capacity to reduce water production and enable a more gradual and controlled increase in gas/oil ratio for most scenarios. The autonomous inflow control valve shows its largest potential to reduce unwanted fluids and increase oil recovery when used in segmented reservoirs. In cases with uncertain aquifer and/or gas cap strength, or large variation in effective permeability, the valve will make an infill well more robust as it autonomously adapts to reality, chokes unwanted fluids, and consequently enables more carbon-efficient reservoir management.

Joint probability analysis of storage reservoir system characteristics

A comprehensive evaluation of the frequency distribution analysis of reservoir storage capacity plays an important role in reducing the uncertainty in the design and operation of reservoir systems. In most studies, a simplified reservoir system (such as over-year system and using annual data, without incorporating evaporation losses and performance measures, i.e. reliability, vulnerability, and constant monthly demand) was investigated. On the other hand, the required reservoir capacity variable is not independent of other characteristics of the reservoir system such as performance measures, critical period, and evaporation losses, and has a high correlation with these variables. Therefore, in this study, the joint probabilities of different characteristics of the reservoir system were comprehensively developed using copulas. Hence, an extensive Monte Carlo reservoir simulation procedure incorporating monthly streamflow data, performance measures, evaporation losses, and demand was applied to extract the population of dependent variables of an actual reservoir. The results showed that a) the required storage capacity in the reservoir system is highly dependent on other reservoir characteristics such as performance measures, critical period, and evaporation losses; b) the joint probability of the reservoir system characteristics are well modeled by a Gaussian copula and c) the joint probability of the reservoir characteristics has a systematic behavior for given reliability and vulnerability values.

Artificial intelligence algorithms for optimizing assisted reproductive technology programs: A systematic review

Artificial intelligence (AI) has been experiencing rapid growth in recent years, and numerous applications are improving the single-step efficiency of the whole assisted reproductive technology (ART) procedure. In this review, we collected all the algorithms supplying ART and selected those supporting the clinical assistance to the procedure up to the successful attempt. Those with a clear role in improving ART performances were further selected. We found a questionnaire-based algorithm identifying patients at risk for endometriosis with early management and better fertility outcome. An algorithm can detect the values of simple gamete production (male) and reservoir (female) according to gradual scale allocation, and display themas normal or abnormal, spontaneousor stimulated gamete production. This can provide significant benefits for infertile couples undergoing diagnostic and therapeutic journeys. The calculators for the starting dose of gonadotropins and the trigger timing during controlled ovarian stimulation make clinical management more efficient. With the application of AI in ART, the ability to determine the optimal number of metaphase II oocytes required for blastocyst formation and number of oocytes needed for embryo production has been significantly improved. The calculation of the implantation rate as proposed in different calculators, using the ultrasound of endometrial vascularization or the age and euploidy of the embryo transferred, may provide further advancement in managing the ART procedure with more participation from the couples to increase the efficacy of the procedures. Finally, the calculator of presumptive success with an ART program based on couples or medical center profiling and efficiency is of tremendous comfort to couples. In conclusion, algorithms and machine learning development in human reproduction are growing daily with evident benefits. Infertility treatments by in vitro fertilization (IVF) are assisted by several algorithms that improve the efficiency of each procedure step, making IVF program’s management more effortless.

Towards a workflow to evaluate geological layering uncertainty on [formula omitted] injection simulation

We propose a workflow for updating 3D geological meshed models to test different layering scenarios and to assess their impact on the simulation of CO2 injection. This workflow operates on a tetrahedral mesh that encodes rock unit information as well as rock physical properties. The alternative layering meshes are built by modifying the input mesh and inserting a new horizon defined by a scalar field. Modifying consistently a 3D meshed model while keeping its quality is a challenge that we tackle using the advanced capabilities of MMG, an open source remeshing library. CO2 injection is then simulated with GEOSX, an open-source, multiphysics, and multilevel simulation solver. We demonstrate this workflow for stratigraphic layering uncertainty assessment on a simple synthetic layered reservoir on the flank of a salt diapir. Comparison of simulation results is eased since modifications of the mesh are localized to the area around the inserted horizon. The consistent results highlight the role of stratigraphic unconformities for trap integrity. This work opens a promising path for developing numerical simulation of CO2 injection on unstructured meshes by combining advanced coupled flow-geomechanical models in geological domains affected by structural uncertainties.

Compositional Mixing Characteristics of Separate Gas and Oil Charges into Oil Field Reservoirs

Many reservoirs worldwide received separate charges of primary biogenic gas and oil. In high pressure provinces such as the Gulf of Mexico, the added gas should be in solution if thoroughly mixed. In addition, this biogenic gas is easily differentiated from thermogenic gas by measurement of methane carbon isotopes and gas composition. Consequently, the extent of mixing can be determined with accuracy, and the gas and oil mixing processes depend on specifics of reservoir charging and can lead to multiple outcomes. The conventional expectation is that the biogenic gas always precedes oil entry into the reservoir. Here, chemical evaluation of reservoir fluids coupled with reservoir understanding of several reservoirs establishes that the oil can arrive prior to biogenic gas. Seismic imaging of gas chimneys provides a plausible explanation for how this can occur. In addition, thermodynamic and geochemical evaluation of the reservoir fluids across the reservoir tightly constrains possible mixing processes. Simple reservoir simulation shows that excellent gas and oil mixing can occur over a wide range of conditions provided that the gas charges into the oil column at its bottom (at the oil–water contact). This modeling exhibits simple fluid mechanics expectations for this reservoir charging process. In addition, since the increase of solution gas destabilizes asphaltenes, fluid geodynamic processes are indicated that can lead to redistributions of asphaltenes. Evaluation of gas and oil mixing processes in reservoirs fits within the framework of the powerful new technical discipline, reservoir fluid geodynamics, for subsurface characterization.

Capacity Analysis and Understanding of Pilot Test Well in Gas Storage A

There is a relatively simple gas reservoir structure in gas storage A, with good trap sealing, large cap rock thickness, relatively inactive fault activity, good overall reservoir connectivity, large storage capacity, water invasion occurs in only a few wells in the low part of the structure, so there are the geological conditions for building gas storage reservoir. This paper analyzed the capacity and maximum recovery capacity of horizontal wells in this block, and the pressure influence range during the recovery process of high-speed pilot production through the pilot test of reservoir construction; moreover, it got the following understanding: the horizontal wells with good development effect, high recovery capacity, and the pressure influence range in this block are mainly within 400m from the well distance; there are differences in the gas production capacity of different types of reservoirs, and the capacity contribution of Class I reservoirs is large, while the capacity contribution of Class II and III reservoirs is small, together these believe that Shengping gas storage has the capacity conditions for building reservoir.