Numerical Reservoir Simulation Research Articles

Assessing a potential site for offshore CO2 storage in the Weixinan Sag in the northwestern Beibu Gulf Basin, northern South China Sea

AbstractGeologic carbon storage (GCS) activities, especially offshore, are still far insufficient worldwide. Sedimentary basins in the northern South China Sea (nSCS) are identified to be favorable to offshore GCS deployment. This study investigates by numerical reservoir simulations the performance of the Weixinan Sag in the northern depression of the Beibu Gulf Basin (BGB), which is considered the most promising area for CO2 offshore GCS in saline formations. Simulations of CO2 injection at a potential site with a normal fault indicate that the pressure buildup induced by CO2 horizontal injection could penetrate extremely low‐permeability faults or caprock formations. The CO2 plumes under higher injection pressures are more constrained in the horizontal direction and hence appear thicker while CO2 tends to spread out horizontally in conditions of low pressure. The impermeable fault renders the CO2 plume about 200 m smaller in size horizontally. Assuming the presence of a fault with a highly permeable core causes leakage to occur after about 30 years of injection, which accounts for only 0.04% the injected amount. For the vertical‐well injection case, the potential CO2 leakage only accounts for around 0.28% of the injection amount, which is far less than the criterion (i.e., 2%) required to make GCS worthwhile. The storage capacities of the formations are mainly controlled by the depths and thicknesses since both their porosities and permeabilities are comparable. The formation Jiaowei‐2 and Xiayang have the largest and second largest storage capacities, respectively, when using a fully perforated well for vertical injection. The average storage capacity of the studied site is 13.04 kg m−3, which is comparable to that of the formation Xiayang. Average injectivities of formations Jiaowei‐2, Xiayang, Weizhou‐1, and Weizhou‐3 are 8.29 × 10−5, 1.75 × 10−4, 6.58 × 10−5, and 2.99 × 10−3 kg s−1 Pa−1, respectively. © 2022 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons Ltd.

Capacitance Resistance Clustered Model for Mature Peripheral Waterflood Performance Prediction & Optimization

Optimizing water injection rate distribution in waterflooding operations is a vital reservoir management aspect since water injection capacities may be constrained due to geographic location and facility limitations. Traditionally, numerical grid-based reservoir simulation is used for waterflood performance evaluation and prediction. However, the reservoir simulation approach can be time-consuming and expensive with the vast amount of wells data in mature fields.
 Capacitance Resistance Model (CRM) has been widely used recently as a data-driven physics-based model for rapid evaluation in waterflood projects. Even though CRM has a smaller computation load than numerical reservoir simulation, large mature fields containing hundreds of wells still pose a challenge for model calibration and optimization. In this study, we propose an alternative solution to improve CRM application in large-scale waterfloods that is particularly suitable for peripheral injection configuration. Our approach attempts to reduce CRM problem size by employing a clustering algorithm to automatically group producer wells with an irregular peripheral pattern. The selection of well groups considers well position and high throughput well (key well). We validate our solution through an application in a mature peripheral waterflood field case in South Sumatra. Based on the case study, we obtained up to 18.2 times increase in computation speed due to parameter reduction, with excellent history match accuracy.

Numerical Simulation of the Oil Production Performance of Different Well Patterns with Water Injection

Numerical reservoir simulation, which includes the construction and operation of a model that performs similarly to a real-world reservoir, is an effective method for exploring complex reservoir issues. Due to the complexity of constructing reservoir environments for experiments, numerical simulation is a vital method for studying flow behavior under reservoir conditions. In this study, a black-oil modeling simulator was used to construct, simulate, and evaluate a conceptual hydrocarbon reservoir model. The model evolved by drilling two production wells and one injection well in two cases. The first case consisted of two horizontal production wells and one injection well, while the second consisted of two vertical production wells and an injection well. In total, 25 simulation runs were performed, and the results showed that horizontal wells perform better than vertical wells in terms of productivity, with a field oil production total of 1,930,000 m3. This is significantly higher than vertical wells, which have a field oil production total of 1,890,000 m3 after 1840 days. The field recovery factor for horizontal wells was 41% and for vertical wells it was 39%, both of which were less than 50%. This indicates that the reservoir’s sweeping efficiency was minimal. To enhance sweeping efficiency, the water injection rate and number of injection wells should be increased, as well as well patterns and locations remodeled. It was also shown that as reservoir thickness increased, horizontal and vertical well productivity increased. In order to boost horizontal well productivity and increase field oil recovery above 50%, the horizontal well length should be increased to take up a wider area of the reservoir portion. On the other hand, well length may have no impact on vertical well production efficiency.

Application of multicomponent sorption for diffusive transport in numerical simulation of tight reservoirs

One of the main challenges in modeling compositional transport in tight formations is to include the complex interaction of chemical species with rock surfaces. Such interactions result in compositional variations near the surfaces, which can significantly affect the compositional diffusive transport through tight pores.This paper presents the application of the Multicomponent Potential Theory of Adsorption (MPTA) for the fluid compositions in the central and near-wall sorbed layers subject to a wall chemical potential. The two-layer approximation of MPTA is implemented in the diffusive transport simulation that computes the mass transfer driven by fugacity gradient based on the dusty gas model for the central and sorbed layers of pores. This simulator uses a partially implicit formulation to solve the multiphase multicomponent mass transfer equations including the sorbed layer.The main novelty of this paper lies in the detailed analysis of the interplay between fluid components and rock surfaces, which became possible with the two-layer MPTA coupled with multicomponent diffusion. The diffusion simulation for a ternary case of methane, n-butane, and n-decane with the approximate MPTA shows that the sorption and capillary pressure can cause the compositional segregation between the central and sorbed layers. The segregation enhances the rates of methane injection and n-decane production in their counter-current diffusion. Methane (the lightest) is transported deep into the reservoir by diffusing through the central layer while n-decane (the heaviest) is diffused primarily through the sorbed layer. n-Butane (the intermediate) does not show preferential partitioning into either layer, resulting in relatively inefficient transport. In the absence of sorption and capillary pressure, the countercurrent diffusion occurs between methane (the lightest) and n-butane (the intermediate) while n-decane (the heaviest) remains nearly immobile. That is, whether the simulation considers the surface-fluid interactions can substantially affect the compositional transport (e.g., produced fluid composition) through tight porous media.The multicomponent Langmuir model is often used for computing the sorbed excess in micropores. However, results show that the Langmuir model becomes physically inconsistent at high pressures because it predicts a monotonically increasing function of pressure. The new approach developed in this research is sufficiently flexible and reasonably accurate for modeling multicomponent sorption at high pressures.

Unified viscoelastic injectivity model: Analytical solutions predicting polymer excess pressure and fracture initiation

The most pressing question to embark on polymer flood projects is whether a polymer can be injected at economic rates while below reservoir parting pressure. There are several published examples of field polymer floods with unexpected injectivity and/or fracture initiation/propagation. Current commercial reservoir simulators lack the capability to model viscoelastic polymer rheology and fracture initiation and often lead to misleading expectations. The laboratory tests to select a polymer are performed without considering fracture or shear-thickening behavior near wellbores. This paper presents a physical-mathematical model to estimate excess pressure due to viscoelastic polymers and assist field operators in better polymer selection and injection design. One of the primary purposes of this paper is to develop predictive analytical tools based on the well-known Unified Viscoelastic Model (UVM), capable of estimating excess pressure and fracture initiation, independent of numerical solutions and reservoir simulators. Asymptotic techniques and special functions theory are utilized to construct a set of general closed-form analytical solutions. It is found that viscoelastic pressure drop can be determined by merely knowing the polymer rheology and slug radius from the wellbore. The derived viscoelastic pressure drop solutions can be easily implemented in MS Excel, which is convenient for engineers participating in the design and execution of polymer flooding projects. Analytical solutions are compared with numerical solutions, laboratory experiments, and field data, and excellent agreement is found.

Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

Well production optimization is a complex and time-consuming task in the oilfield development. The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production. This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency. To improve optimization efficiency, a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization (BADS) algorithm is established. This new objective function, which represents the water flooding potential, is extracted from streamline features. It only needs to call the streamline simulator to run one time step, instead of calling the simulator to calculate the target value at the end of development, which greatly reduces the running time of the simulator. Then the well production optimization model is established and solved by the BADS algorithm. The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples. Results demonstrate that the new objective function is positively correlated with the cumulative oil production. And the BADS algorithm is superior to other common algorithms in convergence speed, solution stability and optimization accuracy. Besides, this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods. It can provide a more effective basis for determining the optimal well production for actual oilfield development.

Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron

Machine learning (ML) has been a technique employed to build data-driven models that can map the relationship between the input and output data provided. ML-based data-driven models offer an alternative path to solving optimization problems, which are conventionally resolved by applying simulation models. Higher computational cost is induced if the simulation model is computationally intensive. Such a situation aptly applies to petroleum engineering, especially when different geological realizations of numerical reservoir simulation (NRS) models are considered for production optimization. Therefore, data-driven models are suggested as a substitute for NRS. In this work, we demonstrated how multilayer perceptron could be implemented to build data-driven models based on 10 realizations of the Egg Model. These models were then coupled with two nature-inspired algorithms, viz. particle swarm optimization and grey wolf optimizer to solve waterflooding optimization. These data-driven models were adaptively re-trained by applying a training database that was updated via the addition of extra samples retrieved from optimization with the proxy models. The details of the methodology will be divulged in the paper. According to the results obtained, we could deduce that the methodology generated reliable data-driven models to solve the optimization problem, as justified by the excellent performance of the ML-based proxy model (with a coefficient of determination, R2 exceeding 0.98 in training, testing, and blind validation) and accurate optimization result (less than 1% error between the Expected Net Present Values optimized using NRS and proxy models). This study aids in an enhanced understanding of implementing adaptive training in tandem with optimization in ML-based proxy modeling.

Research and Application of Waterflooding Mechanism at the Bottom of Ultra-Low Permeability Shallow Reservoir with Horizontal Fractures

The Yanchang Eastern Oilfield of Ordos Basin is a typical ultra-low permeability shallow reservoir. Because of the relatively low vertical pressure, horizontal artificial fractures are prone to take place in the case of oil well fracturing. Given the bigger contact surface between the horizontal fracture and the waterflood front of the water injection well, the oil well may be flooded fast in a short time, leading to a low mobilization degree of the reservoir. According to the characteristics of waterflooding of horizontal fractures, the development mode of waterflooding at the bottom of oil reservoirs was proposed. Through core sample displacement experiments and nuclear magnetic resonance online tests, combined with numerical simulation of reservoirs, field tracer test, and other comprehensive methods, the heterogeneity of reservoirs and the limit of waterflooding parameters were optimized. The research results show that the waterflooding effect is the best when the variation coefficient of permeability is less than 0.5, the permeability rush coefficient is less than 1.5, the permeability contrast is less than 5, the waterflooding rate is 0.06 mL/min, and the waterflooding pressure is 7 MPa. Through field practice, the daily oil production of a single well in the test well group increased from 0.054 t/d before the test to 0.179 t/d, the water cut decreased from 15% before the test to 10%, the formation pressure increased by 0.18 MPa, and it is predicted that the final recovery would increase by 2%. This study provides an experimental and theoretical basis for water injection development of ultra-low permeability shallow reservoirs with horizontal fracture, and also plays a good demonstration role for high-efficiency water injection development of shallow reservoirs.

Spontaneous imbibition characteristics of shale oil reservoir under the influence of osmosis

The spontaneous imbibition (SI) process in shale oil reservoirs is not only influenced by capillary force, but also by the osmotic pressure between the fracturing fluid and formation water in the nanopores media. In this study, experimental methods are used to investigate the mechanisms of osmosis in the SI, taking into account the presence of initial formation water in shale oil reservoirs. To investigate the effect of osmosis, SI experiments were performed on the fine-grained felsic shale of the Qikou sag of Dagang oilfield. Low-field NMR testers and high-precision electronic balances are utilized for the measuring of oil–water migration. The results show that, when Sw ≠ 0, high-salinity fluid SI can be divided into four stages: initial imbibition stage, drainage stage, secondary imbibition stage and stationary stage; when Sw = 0, there is no drainage stage of high-salinity fluid SI; when Sw ≠ 0 or Sw = 0, low-salinity fluid SI can be called the “osmosis-enhanced SI”; and we have found that “newly formed pores or microfractures” as well as reducing salinity can promote SI. This article presents a systematic study of SI of shale oil reservoirs under the influence of osmosis, which provide useful information for reservoir numerical simulation and development program design.

Study on Enhancing Shale Oil Recovery by CO2 Pre-Pad Energized Fracturing in A83 Block, Ordos Basin

The Ordos Basin is rich in shale oil resources. The main targeted layers of blocks A83 and X233 are the Chang 7 member of the Yanchang Formation. Due to extremely low permeability, a fracturing technique was required to enhance oil recovery. However, after adopting the stimulated reservoir volume-fracturing technology, the post-fracturing production of the A83 block is significantly lower than that of the X233 block. For this problem, the dominating factors of productivity of the two blocks were analyzed using the Pearson correlation coefficient (PCC) and the Spearman rank correlation coefficient (SRCC), showing that the main reason for the lower production of the A83 block is its insufficient formation energy. To solve this problem, the CO2 pre-pad energized fracturing method was proposed. To study the feasibility of CO2 pre-pad energized fracturing in the A83 block, an integrated reservoir numerical simulation model of well A83-1 was established based on the idea of integration of geology and engineering. Additionally, the productions within five years after conventional volume fracturing and CO2 pre-pad energized fracturing were compared. The results show that compared with conventional volume fracturing, the cumulative oil production of CO2 pre-pad energized fracturing increases by 11.8%, and the water cut decreases by 16.5%. The research results can guide the subsequent reservoir reconstruction operation in the A83 block and provide new ideas for fracturing in the future.

Numerical upscaling of nonlinear flow in low-permeability sandstone matrix system

The matrix system's apparent permeability model based on nano- and micro-scale mechanism modeling is limited in its role in low-permeability reservoir numerical simulation due to the lack of suitable up-scaling methods. This paper developed a numerical simulation method to evaluate the microporous structure's apparent permeability contribution to total permeability of the heterogeneous representative elementary volume (REV)-scale matrix under macroscopic pressure gradients. First, a local apparent liquid permeability (LALP) model considering the heterogeneous microporous structure and multiple water-transport mechanisms was established to describe the nonlinear seepage behavior of fluid in microscopic computational domain. Then, a multiresolution-image-based REV-scale domain reconstruction method was proposed, in which the microporous structural characteristics were quantified by fractal heterogeneous-porous-media model, and the REV-scale matrix was subsequently stochastic reconstructed based on the porosity distribution characteristics of microporous structures. Finally, the microscopic LALP model and macroscopic continuum model were fully coupled to numerical simulate the nonlinear seepage progress in REV-scale matrix domain. The comparisons between simulation results and experimental results demonstrated that the developed numerical simulation method can effectively predict the nonlinear flow velocity curve of REV-scale sandstone matrix. The influences of internal factors (microporous structural characteristics and seepage fluid properties) and external factors (temperature and pressure) on absolute permeability and apparent permeability of REV-scale matrix were investigated. The results demonstrated that (1) microporous structural characteristics determines the approximate level of intrinsic reservoir permeability, especially the average maximum pore size of discrete domain; (2) with microporous structure's permeability distribution heterogeneity increasing, the macroscopic intrinsic permeability decreases and the differences between apparent permeability and intrinsic permeability increase.

Well control optimization in waterflooding using genetic algorithm coupled with Artificial Neural Networks

Optimum well controls to maximize net present value (NPV) in a waterflooding operation are often obtained from an iterative process of employing numerical reservoir simulation and optimization algorithms. It is often challenging to implement gradient-based optimization algorithms because of the large number of variables and the complexities to embed the optimization algorithm in the simulator solving workflow. Approaches based on repeated model evaluation are easier to implement but are often time-consuming and computationally expensive.

A comparative study on outlier detection techniques for noisy production data from unconventional shale reservoirs

Decline curve analyses (DCA) and rate transient analyses (RTA) are widely used to characterize the fluid flow through porous media and forecast future production. Oil and gas production data are routinely analyzed for history matching and optimizing the well stimulation methods in hydrocarbon exploration and production lifecycle. However, outliers add significant uncertainty and non-uniqueness to results from production data analysis. This study provides a structured and comprehensive overview of five widely used outlier detection (OD) techniques for identifying and removing outliers in production data. Each OD technique measures deviation differently and, therefore, has a different outcome even when applied to the same dataset, creating the need to test several methods and find the optimal technique for identifying and removing outliers from production data. First, we generated production data from a typical multi-fractured horizontal well using a numerical reservoir simulator and added random noise to the data. Then, we used five different OD techniques to identify the prelabeled outliers from the synthetic production data. Finally, we identified the best-performing OD algorithm by comparing the various evaluation metrics such as the mean absolute error (MAE), precision, sensitivity, and F1 score. Results showed that the angle-based OD (ABOD) had the best MAE, precision, sensitivity, and F1 score of 8%, 85%, 98%, and 0.90, respectively. The next best-performing OD technique was distance-based OD (DBOD), with MAE, precision, sensitivity, and F1 score of 16%, 71%, 100%, and 0.83, respectively. We tested the ABOD method on several field production datasets by assuming different outlier thresholds (fraction of the data points likely to be outliers). Visual inspection of the processed data showed that the ABOD method effectively identified and removed the outliers from a relatively clean dataset (outlier threshold of 20%) and a highly noisy dataset (outlier threshold of 80%). This algorithm is intuitive and can effectively identify and remove outliers from the field production data to improve production forecasting, reserves estimation, and rate transient analysis for multi-fractured horizontal oil and gas reservoirs.

Research on Remaining Oil Characterization in Superheavy Oil Reservoir by Microgravity Exploration

Some physical processes such as oil and gas development, metal deposit collection, and groundwater resource migration can cause density changes, for which microgravity monitoring is the most intuitive method to monitor the density change process. Based on the basic principle of microgravity measurement and the idea of multiscale separation, a multiscale, second-order, surface-fitting, residual gravity anomaly extraction method is proposed to separate superimposed microgravity fields. In this method, regional fields of different scales are fitted and calculated successively with the measurement points as the center, so as to separate the gravity anomalies produced by different-depth density bodies. Results from actual data show that this method extracts the reservoir’s residual density characteristics of plane gravity anomaly on the basis of remaining oil distribution characteristics, consistent with reservoir numerical simulation results. A three-dimensional least-squares inversion of the method for extracting residual gravity anomaly was carried out, with the inversion results consistent with the results of vertical remaining oil distribution characteristics and well-test production results.

Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory

Oil saturation is a kind of spatiotemporal sequence that changes dynamically with time, and it is affected not only by the reservoir properties, but also by the injection–production parameters. When predicting oil saturation during water and gas injection, the influence of time, space and injection–production parameters should be considered. Aiming at this issue, a prediction method based on a controllable convolutional long short-term memory network (Ctrl-CLSTM) is proposed in this paper. The Ctrl-CLSTM is an unsupervised learning model whose input is the previous spatiotemporal sequence together with the controllable factors of corresponding moments, and the output is the sequence to be predicted. In this way, future oil saturation can be generated from the historical context. Concretely, the convolution operation is embedded into each unit to describe the interaction between temporal features and spatial structures of oil saturation, thus the Ctrl-CLSTM realizes the unified modeling of the spatiotemporal features of oil saturation. In addition, a novel control gate structure is introduced in each Ctrl-CLSTM unit to take the injection–production parameters as controllable influencing factors and establish the nonlinear relationship between oil saturation and injection–production parameters according to the coordinates of each well location. Therefore, different oil saturation prediction results can be obtained by changing the injection–production parameters. Finally, experiments on real oilfields show that the Ctrl-CLSTM comprehensively considers the influence of artificial controllable factors such as injection–production parameters, accomplishes accurate prediction of oil saturation with a structure similarity of more than 98% and is more time efficient than reservoir numerical simulation.

Electrically-enhanced THAI in situ combustion technology for upgrading and production of heavy oil

Nearly 70% of the global oil reserves are made up of unconventional oils such as heavy oils, bitumen, and tar sand. However, they are yet to be fully exploited as a result of the capital-, energy-, water-, wastewater-, and carbon-intensive nature of their development. To be in consistent with the current goals of climate crisis mitigations, technologies that are more environmentally friendly with high efficiencies are needed. This is where the toe-to-heel air injection (THAI) in situ combustion process comes in. However, experiments and semi-commercial operations have shown that THAI is a low-oil-production-rate process where there was nonlinear relationship between air injection rate and oil production rate. Therefore, to find out about the feasibility of adding an extra source of heat in order to improve oil production rates and with the second aim of testing the possibility of heating an annular catalyst layer surrounding the horizontal producer (HP) well such as in the in situ catalytic upgrading process (CAPRI™), a first-of-a-kind study of electrically-enhanced THAI (i.e. eTHAI) process is presented in this paper. The eTHAI involves surrounding the HP well with the electrical heating coils in which induced current flows so that heat is generated and released in the reservoir. Through reservoir numerical simulations using CMG STARS, it is found that it is indeed possible to electrically heat the HP well whilst conducting pre-ignition steam injection and subsequently operating in situ combustion. Regardless of the rate of heating, it is found that the shape and the stability of the combustion front are not affected by the incorporation of the electrical heating around the HP wellbore. In the eTHAI process, addition of extra heat from the electrical heating coils resulted in increased cumulative oil production to 32,000 m3 compared to 25,000 m3 in the base case model over the 834 days of process time. This is an increase in oil recovery by 28% over the same period compared to in the base case model. It is concluded that the resulting acceleration in oil production rates and recoveries implies earlier realisations of returns on investment. Moreover, it is found that the higher the rate of electrical heating, the wider the length of the HP well used for mobilised oil production (i.e. the wider the thickness of the mobile oil zone (MOZ)). It is also discovered that the higher the electrical heating rate, the less the air cooling effect and in turn the higher the quantity of heat that is distributed inside the reservoir. As a result, it is concluded that the increased distribution of higher quantity of heat by the heated air coming from the outlet zone of the VI wells of the eTHAI models results in increased oil mobilisation and production rates. However, future work should investigate the limiting electrical heating rate beyond which no appreciable increase in oil production rates is achievable. Finally, it is shown that electrically-enhanced THAI process operates stably, efficiently, and selective sectional heating of catalyst-surrounded wellbore is indeed feasible.

A Meshless Numerical Modeling Method for Fractured Reservoirs Based on Extended Finite Volume Method

Summary In this paper, a meshless numerical modeling method named mesh-free discrete fracture model (MFDFM) of fractured reservoirs based on the newly developed extended finite volume method (EFVM) is proposed. First, matching and nonmatching point cloud generation algorithms are developed to discretize the reservoir domain with fracture networks, which avoid the gridding challenges of the reservoir domain in traditional mesh-based methods. Then, taking oil/water two-phase flow in fractured reservoirs as an example, MFDFM derives the EFVM discrete scheme of the governing equations, constructs various types of connections between matrix nodes and fracture nodes, and calculates the corresponding transmissibilities. Finally, the EFVM discrete scheme of the governing equations and the generalized finite difference discrete scheme of various boundary conditions form the global nonlinear equations, which do not increase the degree of nonlinearity compared with those in the traditional finite volume method (FVM)-based numerical simulator. The global equations can be solved by the existing nonlinear solver in the FVM-based reservoir numerical simulator by only adding the linear discrete equations of boundary conditions, which reduce the difficulty of forming a general purpose MFDFM-based fractured reservoir numerical simulator. Several numerical test cases are implemented to illustrate that the proposed MFDFM can achieve good computational performance under matching and nonmatching point clouds, and for heterogeneous reservoirs, complex fracture networks, complex boundary geometry, and complex boundary conditions, by comparing the computational results of MFDFM with embedded discrete fracture model (EDFM). Thus, MFDFM retains the computational performances of the traditional mesh-based methods and can avoid the difficulties of handling complex geometry and complex boundary conditions of the computational domain, which is the first meshless numerical framework to model fractured reservoirs in parallel with the mesh-based discrete fracture model (DFM) and EDFM.

An inversion model of dynamic inter-well connectivity by using flow splitting method in processing heterogeneity of multilayer reservoirs

Multilayer oil-bearing reservoirs are generally of strong heterogeneity, which hinder the commonly used numerical simulation methods. The existence of interlayer heterogeneity makes the model more complex and poses a higher challenge to the computational accuracy. In order to solve the above-mentioned problems, this article proposes a new calculation model of inter-well connectivity based on production data inversion technology. The model reflects the interlayer relationship by introducing flow splitting coefficients. The objective vector is established with the parameters of inter-well conductivity, dimensionless control volume and flow splitting coefficient. Adam algorithm is used to fit the production data which made the model result closer to the formation result. The mechanism model and real model are applied respectively to verify results in the research process. The model error is 4.9% and 5.6%, respectively, compared with the results of commercial reservoir numerical simulation software. The model provides a new solution to the problem of heterogeneity in multilayer reservoir and assists oilfield with better formulation of production strategy.

Integrated workflow to investigate the fracture interference effect on shale well performance

Horizontal drilling and multistage hydraulic fracturing applied in shale formations over the past decade. The operators are trying even closer cluster spacing to increase the initial rate, but it is at the expense of higher operation costs and complexity. This study presents an integrated workflow to investigate the effect of cluster interference on well performance. Analytical rate transient analysis (RTA) was combined with reservoir numerical simulation to calculate the effective fracture surface area (ACe) for hydrocarbon production. A proxy model was built to estimate the effective to actual stimulated fracture area ratio as a function of completion and reservoir parameters. The integrated workflow was applied to actual field data for two shale gas wells. An economic study was conducted to investigate the optimum spacing based on the well profitability. The well with a higher stage number and tighter cluster spacing had high cluster interference with a low ACe/ACa ratio. The well will drain the production area near the wellbore faster with a high initial production rate but with high production declining rate. Increasing the cluster spacing, with the same injected proppant volume, showed an increase in the ACe/ACa ratio, and a decrease in cluster interference. A lower initial rate was observed with a low production declining rate. Economic study showed optimum spacing of 60 ft based on the formation properties, capital cost, and gas price. As the interest rate, gas prices, and increases or low capital costs, the optimum completion tends to be with the tighter spacing to accelerate the production.

Research Status of and Trends in 3D Geological Property Modeling Methods: A Review

Three-dimensional (3D) geological property modeling is used to quantitatively characterize various geological attributes in 3D space based on geostatistics with the help of computer visualization technology, and the results are often stored in grid data. The 3D geological property modeling includes two main components, grid model generation and property interpolation. In this review article, the existing grid generation methods are systematically investigated, and both traditional and multiple-point geostatistical algorithms involved in interpolation methods are comprehensively analyzed. It is shown that considering the numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model for simulations in petroleum exploration and development. For the interpolation methods aspect, most geological phenomena are nonstationary, to simulate various types of reservoirs; the main development trends are increasing geological constraints and reducing the limitation of stationarity. Both methods have certain constraints, and the multiscale problem of multiple-point geostatistics poses a main challenge to the field. In addition, the deep-learning based method is a new trend in geological property modeling.