Interwell Connectivity Research Articles

Rapid Identification of Interwell Fracture-Cavity Combination Structure in Fracture-Cavity Reservoir Based on Tracer-Curve Morphological Characteristics

Rapid and effective identification of interwell fracture-cavity composite structures is a necessary prerequisite for a detailed and in-depth understanding of interwell connectivity in fracture-cavity reservoirs. Current identification methods and technologies have the problems of being large-scale and low-resolution; in view of these problems, a method is proposed for rapidly identifying interwell fracture-cavity combination structures using tracer-curve morphological characteristics (peak number and characteristics of two wings). Based on concentration models of tracer curves for an interwell single fracture/pipe/cavity, the morphological characteristics of tracer curves were researched in five different series-parallel combination modes consisting of fractures, pipes, and cavities. The tracer curves of fracture-cavity reservoirs are categorized into three types: single sharp peak, single slow peak, and multipeak. Furthermore, a matching relationship between different fracture-cavity combination structures and the morphological characteristics of tracer curves is clarified. The single-sharp-peak curve with basically symmetrical wings reflects that of an interwell single fracture/pipe; the single-slow-peak curve with a steep ascending branch and a slow descending branch (obvious trailing phenomenon) reflects that of an interwell single cavity or fracture/pipe series cavity; the multipeak curve reflects that of an interwell multifracture/pipe/cavity in parallel; according to the flow difference of each branch flow channel, they can be divided into independent multipeak and continuous multipeak forms. Taking tracer monitoring results from a well group in the Tahe oilfield as an example, field application analysis and verification were carried out. The results show that this method is simple and reliable and can provide a fast and effective means for identifying interwell fracture-cavity combination structures. Meanwhile, the research results can lay a foundation for quantitative interpretation modeling of interwell tracers in fracture-cavity reservoirs considering fracture-cavity configuration.

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics, such as carbonate reservoirs or reservoirs with sedimentary facies distribution, it is difficult to implement history matching directly, especially for the ensemble-based data assimilation methods. In this paper, we propose a multi-source information fused generative adversarial network (MSIGAN) model, which is used for parameterization of the complex geologies. In MSIGAN, various information such as facies distribution, microseismic, and inter-well connectivity, can be integrated to learn the geological features. And two major generative models in deep learning, variational autoencoder (VAE) and generative adversarial network (GAN) are combined in our model. Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation (ESMDA) method to conduct history matching. We tested the proposed method on two reservoir models with fluvial facies. The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features, which can promote the accuracy of history matching.

Evaluating Interwell Connectivity in Waterflooding Reservoirs with Graph-Based Cooperation-Mission Neural Networks

Summary Interwell connectivity plays a key role in waterflooding for guiding water injection. The existing works focus on the response relationship between one injection well and one production well. No research has explored the structural information of waterflooding on a well pattern. To address this challenge, this paper proposes cooperation-mission neural networks for interwell connectivity with graph information. Specifically, we propose some assumptions based on the petroleum domain to represent the well pattern with an adjacent matrix of the graph. Then we propose two targets from the view of injection well groups and production well groups. Accordingly, we propose cooperation-mission neural networks from these two aspects to evaluate the interwell connectivity in the well pattern. We test our model from two perspectives: the accuracy of estimation with tracer and the graduality of interwell connectivity. The results demonstrate that our model makes a good performance and achieves the connectivity analysis accuracy rate of 91.4%. Moreover, this study demonstrates that it is practical to evaluate the interwell connectivity with graph.

Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

The flow field obtained from streamline simulation reflects key properties of the reservoir, such as the distribution of the remaining oil and the location of channels. However, in the three-dimensional streamline field, the advantages of streamline simulation are limited. Because numerous streamlines interfere with each other and distribute in a sophisticated way, it is really difficult to infer the connectivity between wells and the flow characteristics of the reservoir. To make a more effective and visualizable description of the flow field, the three-dimensional streamline field has to be simplified. In this paper, principal component analysis (PCA) is applied to parameterize the streamline attributes and reduce the dimensionality of the flow field. After dimension reduction, the principal components of the streamline field can be analyzed by the clustering method. In the clustering procedure, the mainstream lines are selected according to the clustering center, thereby intuitively illustrating the properties of the reservoir. Through experimental verification, the proposed method can characterize the streamlines of the flow field more efficiently and reflect the inter-well connectivity more clearly than the commercial numerical simulator.

Fault characterization and flow barrier detection using capacitance-resistance model and diagnostic plots

Advantageous for its speed and far less data requirements, the Capacitance-Resistance Model has been successfully applied to waterflood performance prediction and optimization, gas flood optimization and reservoir characterization. In this study, a diagnostic plot and an iterative workflow that incorporates geological and well data with calibrated CRM results, were developed for depicting injector-producer communication, thereby characterizing a reservoir of interest. These were validated using three synfield cases. Thereafter, two selected faults in a Far East Oil Field (FEOF) were characterized and sealing baffles identified around these faults. Based on the results, one fault had several sections with varying degrees of communication and sealing baffles on either side of the fault. The second fault was mostly sealing with no sealing baffles on either side. The new diagnostic plot and workflow also quality-checked interwell connectivities from calibrated CRM, thereby substantially improving the fault characterization process. With far fewer and readily available data from oilfields, reduced physics models like CRM and the Diagnostic Plots are tools for cost-effective and speedy reservoir characterization, and to corroborate results of Interference and Tracer Tests, as well as 4D Seismic.

A Method for Identifying Channeling Paths in Low-Permeability Fractured Reservoirs

Often oilfield fractured horizontal wells produce water flowing in multiple directions. In this study, a method to identify such channeling paths is developed. The dual-medium model is based on the principle of inter-well connectivity and considers the flow characteristics and related channeling terms. The Lorentz curve is drawn to qualitatively discern the geological type of the low-permeability fractured reservoir and determine the channeling direction and size. The practical application of such an approach to a sample oilfield shows that it can accurately identify the channeling paths of the considered low-permeability fractured reservoir and predict production performances according to the inter-well connectivity model. As a result, early detection of water channeling becomes possible, paving the way to real-time production system optimization in low-permeability fractured reservoirs.

Embedded discrete fracture modeling: Flow diagnostics, non-Darcy flow, and well placement optimization

Low-velocity non-Darcy flow often occurs in low permeability reservoirs. In this paper, we discuss how to incorporate a non-Darcy flow correction into embedded discrete fracture modeling (EDFM). The resulting method is validated by reproducing type curves containing the characteristic no-flow segment, followed by a nonlinear and a linear segment. Simulation results are also validated against a direct simulation method that uses local grid refinement to (almost) resolve hydraulic fractures. Second, we discuss how grid-based flow diagnostics can be formulated on top of the new EDFM method and used to provide computationally inexpensive predictions of flow patterns, interwell connections, and measures of dynamic heterogeneity. We then demonstrate how such flow diagnostics can be used to optimize well placement and improve oil recovery.

A Model for the Connectivity of Horizontal Wells in Water-Flooding Oil Reservoirs

As current calculation models for inter-well connectivity in oilfields can only account for vertical wells, an updated model is elaborated here that can predict the future production performance and evaluate the connectivity of horizontal wells (or horizontal and vertical wells). In this model, the injection-production system of the considered reservoir is simplified and represented with many connected units. Moreover, the horizontal well is modeled with multiple connected wells without considering the pressure loss in the horizontal direction. With this approach, the production performance for both injection and production wells can be obtained by calculating the bottom-hole flowing pressure and oil/water saturation according to the material balance equation and a saturation front-tracking equation. Some effort is also provided to optimize (to fit known historical production performances) the two characteristic problem parameters, namely, the interwell conductivity and connected volume by means of a SPSA gradient-free algorithm. In order to verify the validity of the model, considering a heterogenous reservoir, three conceptual examples are constructed, where the number ratio between injection and production wells are 1/4, 4/1 and 4/5, respectively. It is shown that there is a high consistency between simulation results and field data.

Analytical determination of interwell connectivity based on interwell influence

Interwell connectivity, an important element in reservoir characterization, especially for water flooding, is used to make decisions for better oil production. The existing methods in literature directly use related data of wells to infer interwell connectivity, but they ignore the influence between different wells. The connection of one well to more than two wells (as is often true in the oil field well pattern) will impact the accuracy of the connectivity analysis. To address this challenge, this paper proposes the Particle Swarm Optimization-based CatBoost for Interwell Connectivity (PSOC4IC) based on relative features to analyze interwell connectivity with the combination of joint mutual information maximization-based denoising sparse autoencoder for inter-feature construction and extraction and PSO-based CatBoost (PSO-CatBoost) for connectivity prediction with high-dimensional noise data. The experimental results show that the PSOC4IC improves analysis accuracy.

Characterization of the amount of swept fraction (i.e. backbone) and unswept fraction (i.e. dangling ends) between two wells in presence of reservoir anisotropy

The reservoir heterogeneity controls interwell connectivity and affect reservoir dynamics. An approach is to use continuum percolation that is adopted to study the flow behavior of low to intermediate net-to-gross reservoirs. In this work, we use reservoir models with a permeability contrast and determine the interwell connectivity between two wells and the remaining unswept oil. The percolation parameters including the mount of recoverable oil connected between two wells and the amount of unswept oil (also referred to as the backbone and dangling end fractions, respectively) that controls fluid displacement (e.g. waterflooding) varies as a function of sandbody size and reservoir size. These properties show a power law function of net-to-gross (i.e. occupation fraction) with some exponents called critical exponents. There exist a few publications on the numerical values of these parameters. The main contribution of this work is to investigate the effects of reservoir anisotropy on the percolation parameters. To determine the backbone fraction connected between two wells we propose flow-based criteria depending on the system size. The results show that the critical exponents for the backbone and dangling ends are in the range of [0.3, 0.45] and [-0.45, -0.20]. It is notified that the limitation to perform simulations on infinite systems results in a range for these exponents, although there exists unique values for infinite systems. Moreover, a sensitivity analysis is implemented to find the correct flow-based criteria for the backbone. The results of this work extend the applicability of the percolation properties curves for anisotropic reservoirs.

Inter-well connectivity detection in CO2 WAG projects using statistical recurrent unit models

Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process.SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases.The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure.The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. The study results in a reliable and efficient data analytics framework that is well-suited for large field applications.

Injection-production optimization of fault-karst reservoir\u2014considering high-speed non-Darcy effect

Fault-karst reservoirs are featured by complex geological characteristics, and accurate and fast simulation of such kind of reservoirs using traditional simulator and simulation methods is pretty hard. Herein, we tried to discrete the complex fault-karst structures into one-dimensional connected units connecting the well, fracture and cave based on reservoir static physical parameters and injection-production dynamics. Two characteristic parameters, conductivity and connected volume, are proposed to characterize the inter-well connectivity and material basis. Meanwhile, the high-speed non-Darcy seepage term is introduced into the material balance equations for well-fracture-cave connected units to describe the actual seepage characteristics within the fault-karst reservoirs, and to better simulate the oil/water production dynamics. Based on this method, a fracture reservoir model of 1 injection-3 production was established. The change of oil–water action law in different injection and extraction systems under two production regimes of fixed production rate and fixed pressure is analyzed. A case study was conducted on S fault zone, where the flow of oil and gas did not follow the Darcy seepage rule and with a β value of 103–104, the single well flow pressure and oil production were perfectly matched with the real data. In addition, connected units with more prominent high-speed non-Darcy features were found to have better connectivity, which might shed light on the more accurate prediction of inter-well connectivity. Moreover, an improved injection-production well pattern and was proposed based on connectivity prediction model and reservoir engineering method to solve the problems of insufficient natural energy supply and overhigh oil production rate in Block S. Furthermore, the injection/production rate as well as the timing and cycle of water injection was predicted and optimized so as to better guide to site operations.

Analysis of Interwell Connectivity of Tracer Monitoring in Carbonate Fracture-Vuggy Reservoir: Taking T-Well Group of Tahe Oilfield as an Example

Carbonate fracture-vuggy reservoirs are one of the hot spots in oil and gas exploration and development. However, it is extremely difficult to describe the internal spatial structure of the fracture-vuggy unit and understand the interwell connection relationship. As a method to measure reservoir characteristics and feedback reservoir production information directly according to the detected concentration curve, interwell tracer technology provides a direct measure for people to understand the law of oil-water movement and reservoir heterogeneity and is widely used in various domestic oil fields. Based on the flow law of tracer and the CFD flow simulation basic model, this paper establishes the physical conceptual model and studies the influence of three physical parameters (the flow velocity of the fluid passing through the connected channel, diameter of the connected channel, and length of the connected channel) on the concentration curve at the outlet. In addition, the influence of different interwell connection modes on tracer concentration was studied and classified scientifically. According to the simulation, the tracer concentration curve can be classified into three types: unimodal curve, bimodal curve, and multimodal curve. Finally, the injection-production well group in the T-well area of the Tahe Oilfield is taken as an example, the connection mode between injection and production wells in this well area is further discussed and has been verified, which can be used as a reference for the connectivity analysis of similar carbonate reservoirs.

Interpretation of interwell connectivity tests in a waterflood system

This study is an extension of a novel technique to determine interwell connectivity in a reservoir based on fluctuations of bottom hole pressure of both injectors and producers in a waterflood system. The technique uses a constrained multivariate linear regression analysis to obtain information about permeability trends, channels, and barriers. Some of the advantages of this new technique are simplified one-step calculation of interwell connectivity coefficients, small number of data points and flexible testing plan. However, the previous study did not provide either in-depth understanding or any relationship between the interwell connectivity coefficients and other reservoir parameters. This paper presents a mathematical model for bottom hole pressure responses of injectors and producers in a waterflood system. The model is based on available solutions for fully penetrating vertical wells in a closed rectangular reservoir. It is then used to calculate interwell relative permeability, average reservoir pressure change and total reservoir pore volume using data from the interwell connectivity test described in the previous study. Reservoir compartmentalisation can be inferred from the results. Cases where producers as signal wells, injectors as response wells and shut-in wells as response wells are also presented. Summary of results for these cases are provided. Reservoir behaviours and effects of skin factors are also discussed in this study. Some of the conclusions drawn from this study are: (1) The mathematical model works well with interwell connectivity coefficients to quantify reservoir parameters; (2) The procedure provides in-depth understanding of the multi-well system with water injection in the presence of heterogeneity; (3) Injectors and producers have the same effect in terms of calculating interwell connectivity and thus, their roles can be interchanged. This study provides flexibility and understanding to the method of inferring interwell connectivity from bottom-hole pressure fluctuations. Interwell connectivity tests allow us to quantify accurately various reservoir properties in order to optimise reservoir performance. Different synthetic reservoir models were analysed including homogeneous, anisotropic reservoirs, reservoirs with high permeability channel, partially sealing fault and sealing fault. The results are presented in details in the paper. A step-by-step procedure, charts, tables, and derivations are included in the paper.

Sparse Neural Networks for Inference of Interwell Connectivity and Production Prediction

Summary A new neural network-based proxy model is presented for prediction of well production performance and interpretation of interwell connectivity in large oil fields. The workflow consists of two stages. The first stage uses feature learning to describe the general input-output relations that exist among the wells and to characterize the interwell connectivity. In the second stage, the identified interwell connectivity patterns are used as network topology to develop a multilayer neural network proxy model, with nonlinear activation functions, to predict the production performance of each producer. The estimation of connectivity patterns in the first stage serves as an interpretable feature-learning step to improve the effectiveness of the proxy model in the second stage. Identification of interwell connectivity is based on the selection property of the ℓ1-norm minimization by promoting sparsity in the estimated connectivity weights. The sparsity of the network is motivated by the domain knowledge that each production well is mainly supported by a few nearby injection wells. That is, a proxy model that allows each producer to communicate with all the other wells in the field is inherently redundant and must have an unknown sparse representation. The sparse structure of the connection weights in the resulting network is detected by promoting sparsity during the training process. Two synthetic numerical examples, with known solutions, are first used to demonstrate the functionality and effectiveness of ℓ1-norm regularization for interwell connectivity identification. The workflow is then applied to a real field waterflooding example in Long Beach to predict oil production and to infer interwell connectivity information. Overall, the workflow provides a proxy model that effectively combines the implicit physical information from simulated data with reservoir engineering insight to identify interwell connectivity and to predict well production trends.

Prediction of interwell connectivity and interference degree between production wells in a tight gas reservoir

Sulige gas field has poor reservoir physical properties and strong heterogeneity. The existing development well pattern is difficult to realize the overall effective production of reserves, especially in block SuX. Therefore, this paper takes SuX block in the east of Sulige as an example to describe an effective method suitable for the development of well pattern in this area. Combined with logging and production data, the connectivity of gas wells in X infill area in the east of Sulige is determined from the aspects of pressure, sand body, logging, performance and well test. By using the method of dynamic and static analysis, the connectivity between wells of main gas reservoir is judged. Through the analysis of flow unit, the discharge area and the stable value of well-controlled reserves are determined. The degree of interference determined by curve fitting and the distribution characteristics of sand bodies are comprehensively analyzed. A new oil well interference degree model defined by mathematical expression of production is established. The interference degree is quantified by gas production through numerical simulation. Based on the comprehensive analysis of the connectivity, interference degree and other factors in the area, it is determined that 500 * 600 well pattern is a reasonable well pattern in SuX block, and the interference degree is about 20%. Based on the analysis of the connectivity between wells and the interference degree between production wells in SuX block, the distribution and connectivity of sand bodies in tight sandstone gas field under complex geological conditions are determined. It provides a reasonable basis for the network encryption development of tight sandstone gas in this block. It provides a powerful technical support for the efficient development of different tight gas reservoirs in Sulige area.

Technology Focus: Production Monitoring (March 2021)

Last year, events accelerated several trends in the energy landscape. Oil and gas prices have remained low, and the industry is focusing more strongly on reducing costs and increasing operational efficiency. Reducing costs is not only about cutting costs today but also about reducing the life-cycle cost per barrel. Implementing innovative technologies that increase recovery requires a small investment but can bring large rewards. Advances in sensor accuracy, computing power, and data analytics unlock innovative use cases for technology for mapping subsurface movements of fluids. Very different technologies provide independent insights into the displacement process in the reservoir—for example, distributed acoustic sensing (DAS) used for periodic 4D seismic, time-lapse borehole microgravity surveys for 4D reservoir fluid mapping, DNA analytics to map interwell connectivity and zonal contributions, and interpreting downhole gauge pressure fluctuations caused by tidal forces to map fluid fronts. The aggregation of these uncorrelated insights helps geologists and reservoir engineers narrow down the number of possible realizations of the reservoir model, better map bypassed resources, and provide forecasts that are more realistic. On the production side, well instrumentation continues to become more affordable over time. For example: distributed temperature sensing, DAS, and downhole gauge lines can be run in a single optoelectric cable, which reduces well complexity and instrumentation costs. Wells can be instrumented at surface for less than $5,000 using wireless technologies. Wireless downhole gauges also can be retrofitted in older completions. There are many ways to leverage technology for improving production and reservoir monitoring and unlocking potential. The OnePetro online library offers a large collection of novel use cases for technology and algorithms applied to production and reservoir monitoring. The challenge now is to transform the way we work to realize the maximum value from such technology.

Injection-production optimization of carbonate reservoir based on an inter-well connectivity model

Carbonate reservoirs are highly heterogeneous. During waterflooding stage, the channeling phenomenon of displacing fluid in high-permeability layers easily leads to early water breakthrough and high water-cut with low recovery rate. To quantitatively characterize the inter-well connectivity parameters (including conductivity and connected volume), we developed an inter-well connectivity model based on the principle of inter-well connectivity and the geological data and development performance of carbonate reservoirs. Thus, the planar water injection allocation factors and water injection utilization rate of different layers can be obtained. In addition, when the proposed model is integrated with automatic history matching method and production optimization algorithm, the real-time oil and water production can be optimized and predicted. Field application demonstrates that adjusting injection parameters based on the model outputs results in a 1.5% increase in annual oil production, which offers significant guidance for the efficient development of similar oil reservoirs. In this study, the connectivity method was applied to multi-layer real reservoirs for the first time, and the injection and production volume of injection-production wells were repeatedly updated based on multiple iterations of water injection efficiency. The correctness of the method was verified by conceptual calculations and then applied to real reservoirs. So that the oil field can increase production in a short time, and has good application value.

Ensemble-based method with combined fractional flow model for waterflooding optimization

Proxy models are widely used to estimate parameters such as interwell connectivity in the development and management of petroleum fields due to their low computational cost and not require prior knowledge of reservoir properties. In this work, we propose a proxy model to determine both oil and water production to maximize reservoir profitability. The approach uses production history and the Capacitance and Resistance Model based on Producer wells (CRMP), together with the combination of two fractional flow models, Koval [Cao (2014) Development of a Two-phase Flow Coupled Capacitance Resistance Model. PhD Dissertation, The University of Texas at Austin, USA] and Gentil [(2005) The use of Multilinear Regression Models in patterned waterfloods: physical meaning of the regression coefficient. Master’s Thesis, The University of Texas at Austin, USA]. The proposed combined fractional flow model is called Kogen. The combined fractional flow model can be formulated as a constrained nonlinear function fitting. The objective function to be minimized is a measure of the difference between calculated and observed Water cut (Wcut) values or Net Present Values (NPV). The constraint limits the difference in water cuts of the Koval and Gentil models at the time of transition between the two. The problem can be solved using the Sequential Quadratic Programming (SQP) algorithm. The parameters of the CRMP model are the connectivity between wells, time constant and productivity index. These parameters can be found using a Nonlinear Least Squares (NLS) algorithm. With these parameters, it is possible to predict the liquid rate of the wells. The Koval and Gentil models are used to calculate the Wcut in each producer well over the concession period which in turn allows to determine the accumulated oil and water productions. To verify the quality of Kogen model to forecast oil and water productions, we formulated an optimization problem to maximize the reservoir profitability where the objective function is the NPV. The design variables are the injector and producer well controls (liquid rate or bottom hole pressure). In this work the optimization problem is solved using a gradient-based method, SQP. Gradients are approximated using an ensemble-based method. To validate the proposed workflow, we used two realistic reservoirs models, Brush Canyon Outcrop and Brugge field. The results are shown into three stages. In the first stage, we analyze the ensemble size for the gradient computation. Second, we compare the solutions obtained with the three fractional flow models (Koval, Gentil and Kogen) with results achieved directly from the simulator. Third, we use the solutions calculated with the proxy models as starting points for a new high-fidelity optimization process, using exclusively the simulator to calculate the functions involved. This study shows that the proposed combined model, Kogen, consistently generated more accurate results. Also, CRMP/Kogen proxy model has demonstrated its applicability, especially when the available data for model construction is limited, always producing satisfactory results for production forecasting with low computational cost. In addition, it generates a good warm start for high fidelity optimization processes, decreasing the number of simulations by approximately 65%.

Analytical Work Flows Enable Continuous Waterflooding Optimization for a Mature Field

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 198586, “A New Continuous Waterflood Operations Optimization for a Mature Oil Field by Use of Analytical Work Flows That Improve Reservoir Characterization,” by Atul Yadav and Anton Malkov, SPE, Wintershall, and Essam Omara, Suez Oil Company, et al., prepared for the 2019 SPE Gas and Oil Technology Showcase and Conference, Dubai, 21–23 October. The paper has not been peer reviewed. In the complete paper, the authors present a novel approach that uses data-mining techniques on operations data of a complex mature oil field in the Gulf of Suez that is currently being waterflooded. Evidence is presented about how salinity data can be used to further justify the linkages between different wells obtained from cross-correlation analysis. The results presented in this research can be adapted to any waterflooded field to optimize recovery at frequent intervals where injection and production data are available continuously. Introduction Mature oil fields typically present challenges of increased water production and water handling. Considering the geological complexity and associated field-performance behavior, reservoir characterization to optimize water flooding is a major challenge. An integrated reservoir study was con ducted to minimize reservoir uncertainties and increase understanding of the field’s performance behavior. The acceptable history-matched model was used to estimate remaining oil potential, maintain and increase current production levels, and optimize the water-injection rate. Generally, history-matched models need to be updated throughout the life of producing fields as new subsurface data are acquired. Such integrated reservoir modeling studies, however, can be time-consuming and do not necessarily enable quicker decision-making around operational activities. The continuous recording of production and injection data presents new opportunities to apply novel analytical techniques to understand interwell connectivity in the reservoir. The current ability to store and analyze data, coupled with advances in the ability to interpret big data sets, has helped create an independent toolkit that provides analysis without the geological model. In addition, geological information such as pre-existing faults and the commingled or disconnected nature of production between different layers can be integrated to obtain and improve analyses from the analytical models. The authors analyze the results using Pearson’s cross-correlation analysis measure to obtain a qualitative analysis of the field. They also apply Spearman’s rank correlation analysis for the discussed field (henceforth named GOS for purposes of this paper) that helps compare injection and production data. The objective is to present a comparison between the analytical and the stream-lined approach to show consistency in reservoir characterization. The effective injector/producer pairs identified form an important component of the field development.