Multi-fractured Horizontal Wells Research Articles

A three-dimensional numerical well-test model for pressure transient analysis in fractured horizontal wells with secondary fractures

During oil and gas reservoir development, multi-stage horizontal wells (MFHWs) and hydraulic fracturing techniques can effectively increase estimated ultimate recovery. However, there still lacks an understanding of the three-dimensional (3D) pressure transient behaviors of multi-stage fractured horizontal wells with secondary fractures. To narrow this gap, a three-dimensional numerical well-test model based on a discrete fracture model and unstructured tetrahedral grids is developed to study the pressure transient behaviors of MFHWs with secondary fractures. The pressure transient solutions of MFHWs with secondary fractures have been demonstrated by model verifications. The results show that the proposed model can accurately capture the complex transient flow around fractures, including early radial flow that is not easily captured by two-dimensional numerical well test models. The proposed model classifies the flow regimes of a MFHW as: wellbore storage and skin effects, early radial flow, bilinear flow, linear flow, elliptical flow, pseudo radial flow, and pseudo-boundary dominated flow. It is found that the fracture geometry has a relatively large effect on the shape of the pressure derivative curve in this work. The hydraulic fracture half-length has the greatest impact on the pressure transient behaviors of the MFHW, followed by fracture height and secondary fracture half-length, as found in this study. Additionally, fracture parameters are evaluated, and actual well testing data are interpreted, taking into account the fracture height. This work is meaningful to understand the three-dimensional pressure transient behaviors of MFHWs with secondary fractures.

Extreme massive hydraulic fracturing in deep coalbed methane horizontal wells: A case study of the Linxing Block, eastern Ordos Basin, NW China

Deep coal seams show low permeability, low elastic modulus, high Poisson’s ratio, strong plasticity, high fracture initiation pressure, difficulty in fracture extension, and difficulty in proppants addition. We proposed the concept of large-scale stimulation by fracture network, balanced propagation and effective support of fracture network in fracturing design and developed the extreme massive hydraulic fracturing technique for deep coalbed methane (CBM) horizontal wells. This technique involves massive injection with high pumping rate + high-intensity proppant injection + perforation with equal apertures and limited flow + temporary plugging and diverting fractures + slick water with integrated variable viscosity + graded proppants with multiple sizes. The technique was applied in the pioneering test of a multi-stage fracturing horizontal well in deep CBM of Linxing Block, eastern margin of the Ordos Basin. The injection flow rate is 18 m3/min, proppant intensity is 2.1 m3/m, and fracturing fluid intensity is 16.5 m3/m. After fracturing, a complex fracture network was formed, with an average fracture length of 205 m. The stimulated reservoir volume was 1 987×104 m3, and the peak gas production rate reached 6.0×104 m3/d, which achieved efficient development of deep CBM.

Machine learning-based fracturing parameter optimization for horizontal wells in Panke field shale oil

In the process of developing tight oil and gas reservoirs, multistage fractured horizontal wells (NFHWs) can greatly increase the production rate, and the optimal design of its fracturing parameters is also an important means to further increase the production rate. Accurate production prediction is essential for the formulation of effective development strategies and development plans before and during project execution. In this study, a novel workflow incorporating machine learning (ML) and particle swarm optimization algorithms (PSO) is proposed to predict the production rate of multi-stage fractured horizontal wells in tight reservoirs and optimize the fracturing parameters. The researchers conducted 10,000 numerical simulation experiments to build a complete training and validation dataset, based on which five machine learning production prediction models were developed. As input variables for yield prediction, eight key factors affecting yield were selected. The results of the study show that among the five models, the random forest (RF) model best establishes the mapping relationship between feature variables and yield. After verifying the validity of the Random Forest-based yield prediction model, the researchers combined it with the particle swarm optimization algorithm to determine the optimal combination of fracturing parameters under the condition of maximizing the net present value. A hybrid model, called ML-PSO, is proposed to overcome the limitations of current production forecasting studies, which are difficult to maximize economic returns and optimize the fracturing scheme based on operator preferences (e.g., target NPV). The designed workflow can not only accurately and efficiently predict the production of multi-stage fractured horizontal wells in real-time, but also be used as a parameter selection tool to optimize the fracture design. This study promotes data-driven decision-making for oil and gas development, and its tight reservoir production forecasts provide the basis for accurate forecasting models for the oil and gas industry.

Study of inter-well interference in shale gas reservoirs by a robust production data analysis method based on deconvolution

In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering, this study proposes a robust production data analysis method based on deconvolution, which is used for multi-well inter-well interference research. In this study, a multi-well conceptual trilinear seepage model for multi-stage fractured horizontal wells was established, and its Laplace solutions under two different outer boundary conditions were obtained. Then, an improved pressure deconvolution algorithm was used to normalize the scattered production data. Furthermore, the typical curve fitting was carried out using the production data and the seepage model solution. Finally, some reservoir parameters and fracturing parameters were interpreted, and the intensity of inter-well interference was compared. The effectiveness of the method was verified by analyzing the production dynamic data of six shale gas wells in Duvernay area. The results showed that the fitting effect of typical curves was greatly improved due to the mutual restriction between deconvolution calculation parameter debugging and seepage model parameter debugging. Besides, by using the morphological characteristics of the log-log typical curves and the time corresponding to the intersection point of the log-log typical curves of two models under different outer boundary conditions, the strength of the interference between wells on the same well platform was well judged. This work can provide a reference for the optimization of well spacing and hydraulic fracturing measures for shale gas wells.

Performance of multistage-fractured horizontal wells with secondary discrete fractures in heterogeneous tight reservoirs

AbstractA significant portion of tight sandstone reservoirs commonly displays intricate fluvial channels or fault systems. Despite various attempts at analytical/semi-analytical modeling of multistage-fractured horizontal wells (MFHWs) in unconventional reservoirs, the majority of studies have focused on scenarios with homogeneous original physical properties, neglecting cases where MFHWs traverse multiple regions in channelized heterogeneous reservoirs. Comprehending the influence of heterogeneous and leaky faults on the performance of MFHWs is essential for efficient development. This study presents an innovative semi-analytical model to analyze the pressure transient behavior of MFHWs with secondary fractures as they traverse multiple regions in banded channel heterogeneous reservoirs, particularly considering the presence of partially-communicating faults. The approach combines the source method and Green’s function method to obtain solutions, introducing a novel technique for discretizing fractures without discretizing interfaces. The effects of the reservoir heterogeneity, partially-communicating faults and fractures system on pressure behavior are analyzed. The results indicate that the pressure behavior of MFHWs passing through regions with different physical properties exhibits distinctive characteristics, differing from both the homogeneous case and the heterogeneous cases where the well does not traverse distinct regions. Permeability heterogeneity influences the curves of all other flow regimes, except the early and late flow regimes. Faults affect transient pressure behavior only when not positioned in the middle of each two primary fractures. Region area heterogeneity primarily influences the medium flow regimes. This work provides valuable insights into the performance of MFHWs in channelized heterogeneous reservoirs, offering technical support for well testing in these reservoirs.

Analytical Method for Production Forecasting During Boundary-Dominated Flow in Unconventional Volatile Oil Reservoirs Using Single-Phase Analogy

Abstract This paper presents an analytical methodology for production forecasting of wells exhibiting boundary-dominated flow in unconventional volatile oil reservoirs. An analytical model using single-phase analogy is established to predict the production rate of a multi-fractured horizontal well under multiphase flow. Pseudo-variables are employed to linearize governing differential flow equations. Therefore, the analytical model for efficiently handling multiphase flow is essentially an advanced adaptation of existing single-phase models, in which we derive new multiphase fluid properties. To calculate the pseudo-variables accurately, we summarize accessible methods for the determination of saturation–pressure (S–P) relation and further provide an appropriate way to calculate multiphase pseudo-variables in unconventional volatile oil reservoirs based on simulation results. The simulation study indicates that the S–P relation given by constant volume depletion data for near-critical volatile oils leads to satisfactory results, and the S–P relation derived from the tank-type model is appropriate for ordinary volatile oils. The analytical solution and associated methods were validated through comparison with results from a compositional simulator; the excellent agreement during boundary-dominated flow demonstrated the accuracy of the analytical methodology. The analytical methodology can greatly reduce computation and is justified to make production forecasting in unconventional volatile oil reservoirs, this tractable methodology should be attractive to the industry.

Optimization of Carbon Sequestration and Carbon Displacement in Fractured Horizontal Wells in Low Permeability Reservoirs

The increasing use of fossil fuels has raised concerns about rising greenhouse gas emissions. Carbon capture, utilization, and storage (CCUS) is one of the most important technologies for achieving net zero carbon emissions. In oil reservoirs, fully understanding their geological characteristics, fluid characteristics, and pressure distribution and injecting CO2 in a reasonable scheme, some remaining oil can be recovered to improve oil recovery and even obtain certain economic benefits. In this paper, we investigate the effect of CCUS implementation in low-permeability reservoirs from both technical and economic aspects. First, based on the parameters of a low-permeability reservoir, a numerical simulation model of a reservoir with gas injection in a multi-stage fractured horizontal well at the top of the reservoir and oil recovery in a multi-stage fractured horizontal well at the bottom is established. Next, four cases of continuous CO2 injection, intermittent CO2 injection, CO2 injection after water flooding, and water alternating gas drive (WAG) are designed to evaluate their effects on CO2 storage and enhanced oil recovery. Finally, an economic evaluation model is developed to evaluate these four cases. The results show that fractured horizontal wells can improve the injection capacity, increase the swept volume of injected gas, cause CO2 to fully contact the crude oil, greatly increase the contact area between the wellbore and crude oil, and greatly improve oil recovery. The WAG injection-production method can effectively inhibit gas channeling, reduce the production gas–oil ratio, improve oil recovery, and, at the same time, bury more CO2 into the reservoir. Its economic benefit evaluation is also the best among the four cases. In addition, the remaining oil distribution and CO2 buried distribution under different injection-production schemes are also analyzed. This study provides a scientific basis for the operation scheme design of CCUS in low-permeability reservoirs.

Research on Production Prediction Method of Multi-stage Fractured Shale Gas Horizontal Well

Abstract Shale gas is a crucial component of unconventional energy. Productivity evaluation of gas wells is essential to ensure efficient and stable production. However, predicting productivity has been challenging due to the complex characteristics of shale gas reservoirs and the use of multi-stage fractured horizontal wells (MFHW) technology in the development process. This study compares traditional production prediction methods with an optimized least squares support vector machine model (LSSVM). The traditional productivity prediction method involves establishing a mathematical model of the MFHW in a fully enclosed rectangular shale formation. The model considers the effects of shale gas adsorption, diffusion, and pressure sensitivity. The model’s analytical solution is obtained using Duhamel’s principle, Laplace transform, and inverse transform. An independent production data analysis software is developed based on the analytical solution of bottom-hole pressure to predict production. To implement the LSSVM model, the model’s input parameters must be determined first. The LSSVM model’s regularization parameters and kernel parameters are obtained through the particle swarm optimization (PSO) algorithm, and the prediction model is established. The model’s matching is evaluated by calculating the coefficient of determination (R2) and the normalized root mean square error (NRMSE). The results indicate that the traditional production capacity prediction method is suitable for stable production in terms of applicability. However, the LSSVM model does not have this limitation and generally provides more accurate predictions throughout the entire production process. For complex shale gas reservoirs that frequently switch wells and use multi-stage fracturing technology, the LSSVM model is more suitable for predicting shale gas well productivity.

Productivity model for multi-fractured horizontal wells accounting for cross-scale flow of gas and total factor characteristics of fractures

Fractured horizontal wells are widely utilized to develop tight gas reservoirs. Accurate productivity prediction of horizontal wells intercepted by complex hydraulically induced fractures is highly challenging due to fracture branching, bending, extending, and reversing in tight rock formations. Accounting for complex tree-shaped fracture geometries in reservoirs, this study proposes a cross-scale model of gas flows from the matrix to fractures. Using the proposed framework, the well productivity can be calculated in fractured gas reservoirs with any pore size and a fracture-controlled unit. The novelty of this study is that the total factor characteristics of fractures (i.e., length ratio a, width ratio b, branching angle θ, and branching level n), pressure drop coupling between the reservoir and fracture, and gas flow regime in different scale channels can be perfectly considered in the proposed model, from which the key contributions to well productivity are derived. Subsequently, the validity of the proposed cross-scale model is evaluated in comparison with the results of Ning et al. model and field well testing. The characteristic length L0,i, fracture effective width wf, stimulated reservoir volume Vf, fracture pressure pf, pressure gradient Gf, effective permeability kf and flow conductivity Cf of tree-shaped fractures are derived using the proposed model. The results show that the well productivity is jointly determined by Gf and Cf. Moreover, the well productivity mostly depends on a and n, and it is little influenced by b. In contrast, it is almost unaffected by θ. Compared to a straight fracture, a tree-shaped fracture with characteristic parameters of a=0.9 and n=3 can dramatically enhance the productivity by 32 % (b=0.5; θ=30°). In contrast, a tree-shaped fracture slightly increases the productivity by only 3 % when b increases from 0.1 to 0.9 (a=0.2; θ=30°; n=3). Furthermore, when θ is increased from 0° to 90°, the productivity of a well with a tree-shaped fracture decreases by 3 % (a=0.2; b=0.6; n=3). Hence, the study findings will contribute to the exploitation of tight gas reservoirs.

A Physics-Informed Neural Network Approach for Surrogating a Numerical Simulation of Fractured Horizontal Well Production Prediction

With the popularity of deep learning (DL), more and more studies are focusing on replacing time-consuming numerical simulations with efficient surrogate models to predict the production of multi-stage fractured horizontal wells. Previous studies on constructing surrogate models for the prediction of the production of fractured horizontal wells often relied on directly applying existing deep learning architectures without incorporating physical constraints into the model. When dealing with the large number of variables necessary for characterizing the properties of fractures, the input variables of proxy models are often oversimplified; meanwhile, lots of physical information is lost. Consequently, predictions are sometimes physically inconsistent with the underlying principles of the domain. In this study, by modifying the traditional Seq2Seq (LSTM–LSTM) deep learning architecture, a physics-informed encoder–decoder (PIED) architecture was developed to surrogate the numerical simulation codes for predicting the production of horizontal wells with unequal-length intersecting hydraulic fractures on a 2D plane. The encoder is a LSTM network, and the decoder consists of LSTM and fully connected layers. The attention algorithm is also applied in the Seq2Seq architecture. The PIED model’s encoder is capable of extracting the physical information related to fractures. And the attention module effectively passes on the most relevant physical information related to production to the decoder during the training process. By modifying Seq2Seq architecture, the decoder of the PIED incorporates the intermediate input, which is the constant production time, along with the extracted physical information to predict production values. The PIED model excels in extracting sufficient physical information from high-dimensional inputs while ensuring the integrity of the production time information. By considering the physical constraints, the model predicts production values with improved accuracy and generalization capabilities. In addition, a multi-layer perceptron (MLP) which is broadly used as a proxy model; a regular Seq2Seq model (LSTM–Attention–LSTM); and the PIED were compared via a case study, and their MAE values were shown to be 241.76, 184.07, 168.81, respectively. Therefore, the proposed model has higher accuracy and better generalization ability. In the case study, a comparative experiment was conducted by comparing LSTM–MLP (with an MAE of 221.50) and LSTM–LSTM to demonstrate that using LSTM as the decoder structure is better for predicting production series. Moreover, in the task of predicting production sequences, LSTM outperforms MLP. The Seq2Seq architecture demonstrated excellent performance in this problem, and it achieved a 48.4% reduction in MSE compared to MLP. Meanwhile, the time cost for build datasets was considered, and the proposed model was found to be capable of training in a small dataset (e.g., in the case study, 3 days were used to generate 450 samples for training.); thus, the proposed model has a certain degree of practicality.

Fracture Spacing Optimization Method for Multi-Stage Fractured Horizontal Wells in Shale Oil Reservoir Based on Dynamic Production Data Analysis

In order to improve the shale oil production rate and save fracturing costs, based on dynamic production data, a production-oriented optimization method for fracture spacing of multi-stage fractured horizontal wells is proposed in this study. First, M. Brown et al.’s trilinear seepage flow models and their pressure and flow rate solutions are applied. Second, deconvolution theory is introduced to normalize the production data. The data of variable pressure and variable flow rate are, respectively, transformed into the pressure data under unit flow rate and the flow rate data under unit production pressure drop; and the influence of data error is eliminated. Two kinds of typical curve of the normalized data are analyzed using the pressure and flow rate solutions of M. Brown et al.’s models. The two fitting methods constrain each other. Thus, reservoir and fracture parameters are interpretated. A practical model has been established to more accurately describe the seepage flow behavior in shale oil reservoirs. Third, using Duhamel’s principle and the rate solution, the daily and cumulative production rate under any variable production pressure can be obtained. The productivity can be more accurately predicted. Finally, the analysis method is applied to analyze the actual dynamic production data. The fracture spacing of a shale oil producing well in an actual block is optimized from the aspects of production life, cumulative production, economic benefits and other influencing factors, and some significant conclusions are obtained. The research results show that with the goal of maximum cumulative production, the optimal fracture spacing is 5.5 m for 5 years and 11.4 m for 10 years. All in all, the fracture spacing optimization and design theory of multi-stage fractured horizontal wells is enriched.

Production induced fracture closure of deep shale gas well under thermo-hydro-mechanical conditions

Deep shale gas reservoirs have geological characteristics of high temperature, high pressure, high stress, and inferior ability to pass through fluids. The multi-stage fractured horizontal well is the key to exploiting the deep shale gas reservoir. However, during the production process, the effectiveness of the hydraulic fracture network decreases with the closure of fractures, which accelerates the decline of shale gas production. In this paper, we addressed the problems of unclear fracture closure mechanisms and low accuracy of shale gas production prediction during deep shale gas production. Then we established the fluid–solid–heat coupled model coupling the deformation and fluid flow among the fracture surface, proppant and the shale matrix. When the fluid–solid–heat coupled model was applied to the fracture network, it was well solved by our numerical method named discontinuous discrete fracture method. Compared with the conventional discrete fracture method, the discontinuous discrete fracture method can describe the three-dimensional morphology of the fracture while considering the effect of the change of fracture surface permeation coefficient on the coupled fracture–matrix flow and describing the displacement discontinuity across the fracture. Numerical simulations revealed that the degree of fracture closure increases as the production time proceeds, and the degree of closure of the secondary fractures is higher than that of the primary fractures. Shale creep and proppant embedment both increase the degree of fracture closure. The reduction in fracture surface permeability due to proppant embedment reduces the rate of fluid transfer between matrix and fracture, which has often been overlooked in the past. However, it significantly impacts shale gas production, with calculations showing a 24.7% cumulative three-year yield reduction. This study is helpful to understand the mechanism of hydraulic fracture closure. Therefore, it provides the theoretical guidance for maintaining the long-term effectiveness of hydraulic fractures.

A theory to model pseudo steady state water production and predict long-term water recovery from fractured reservoirs

The environmental and economic impact of hydraulic fracturing technology in the energy industry remains a growing concern due to large water consumption and poor water recovery in return. In this study, we propose a novel theory to describe and model the drive mechanisms (fracture closure and fluid expansion) responsible for long-term water recovery from hydraulically fractured reservoirs. In the literature, pseudo steady state (PSS) flow of water has been explained and modelled under the assumption of negligible fluid influx from matrix, referred to as "supercharge conditions". The preliminary flowback data analysis of 8 different unconventional reservoirs from North America in this study shows 1) water PSS flow signature is also observed after fluid breakthrough into fractures, and 2) it can be modelled mathematically, verified with long-term field data, and validated with numerical simulation results. These observations suggest that oil and water develop different networks or flow paths through fractures. With time, oil network expands and water network shrinks. Therefore, water continues to be produced from the fractures as oil saturation increases in the fractures with expansion of oil network due to fracture pressure decline. Based on this theory, an integrated semi-analytical workflow is developed by accounting for the changes in the volumes and compressibilities of both fracture and fluid. The workflow was applied on early production data (150–200 days) from six multi-fractured horizontal wells in Eagle Ford. The results show that the workflow can use the early-time data to accurately estimate long-term water recovery (325–475 days) from fractured reservoirs (within 6 % of measured field data). However, frequent long shut-ins during the course of long-term production increases the uncertainty in water recovery prediction.

Hydrocarbon gas huff-n-puff optimization of multiple horizontal wells with complex fracture networks in the M unconventional reservoir

The oil production of the multi-fractured horizontal wells (MFHWs) declines quickly in unconventional oil reservoirs due to the fast depletion of natural energy. Gas injection has been acknowledged as an effective method to improve oil recovery factor from unconventional oil reservoirs. Hydrocarbon gas huff-n-puff becomes preferable when the CO2 source is limited. However, the impact of complex fracture networks and well interference on the EOR performance of multiple MFHWs is still unclear. The optimal gas huff-n-puff parameters are significant for enhancing oil recovery. This work aims to optimize the hydrocarbon gas injection and production parameters for multiple MFHWs with complex fracture networks in unconventional oil reservoirs. Firstly, the numerical model based on unstructured grids is developed to characterize the complex fracture networks and capture the dynamic fracture features. Secondly, the PVT phase behavior simulation was carried out to provide the fluid model for numerical simulation. Thirdly, the optimal parameters for hydrocarbon gas huff-n-puff were obtained. Finally, the dominant factors of hydrocarbon gas huff-n-puff under complex fracture networks are obtained by fuzzy mathematical method. Results reveal that the current pressure of hydrocarbon gas injection can achieve miscible displacement. The optimal injection and production parameters are obtained by single-factor analysis to analyze the effect of individual parameter. Gas injection time is the dominant factor of hydrocarbon gas huff-n-puff in unconventional oil reservoirs with complex fracture networks. This work can offer engineers guidance for hydrocarbon gas huff-n-puff of multiple MFHWs considering the complex fracture networks.

Model and Analysis of Pump-Stopping Pressure Drop with Consideration of Hydraulic Fracture Network in Tight Oil Reservoirs

The existing pump-stopping pressure drop models for the hydraulic fracturing operation of tight oil reservoirs only consider the main hydraulic fracture and the single-phase flow of fracturing fluid. In this paper, a new pump-stopping pressure drop model for fracturing operation based on coupling calculation of the secondary fracture and oil-water two-phase flow is proposed. The physical model includes the horizontal wellbore, the fracture network and the tight oil reservoir. Through the numerical simulation and calculation, the wellbore afterflow performance, the crossflow performance between the main hydraulic fracture and the secondary fracture, the fracturing fluid leakoff and the oil-water replacement after termination of pumping are obtained. The pressure drop characteristic curve is drawn out by the bottom-hole flow pressure calculated through the numerical simulation, and a series of analyses are carried out on the calculated pressure drop curve, which is helpful to diagnose the -oil-water two-phase flow state and the fracture closure performance under the control of the fracture network after hydraulic fracturing pumping. Finally, taking a multi-stage fractured horizontal well in a tight oil reservoir in the Junggar basin, China as an example, the pump-stopping pressure drop data of each stage after hydraulic fracturing are analyzed. Through the history fitting of the pressure drop characteristic curve, the key parameters such as fracture network parameters, which include the half-length of main hydraulic fracture, the conductivity of main hydraulic fracture and the density of secondary fracture, the fracture closure pressure are obtained by inversion, thus, the hydraulic fracturing effect of fractured horizontal well in tight oil reservoirs is further quantified.

Fracture Conductivity Work Flow Models Well Spacing, Completions Design

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 2023-3864710, “A Comprehensive Simulation Study of Hydraulic Fracturing Test Site 2 (HFTS-2): Part I—Modeling Pressure-Dependent and Time-Dependent Fracture Conductivity in Fully Calibrated Fracture and Reservoir Models,” by Han Li, Jichao Han, SPE, and Jiasen Tan, SPE, Occidental Petroleum, et al. The paper has not been peer reviewed. _ Combining fracture and reservoir diagnostic analysis with integrated geomechanics and reservoir simulation is an efficient and cost-effective approach to generate realistic fracture geometry, understand fluid flow behavior, and define fracture-conductivity distribution in unconventional reservoirs. The complete paper presents a case study of integrated geomechanical and reservoir simulation with a developed fracture-conductivity-calculation work flow that was validated with diagnostic results to evaluate well spacing and completions design. Introduction This study extends that of previous authors by matching field fracture diagnostics and reservoir simulation using variable fracture conductivity. In the example used by the authors from the Hydraulic Fracturing Test Site 2 (HFTS-2) development, multiple fracture diagnostic methods were used to calibrate hydraulic fracture models. Once the model was calibrated, a new proppant-conductivity algorithm assigned conductivity values along the hydraulic fractures based on a physics-based model calculation of proppant concentration. Multiple mechanisms, such as stress- and pressure-dependent effects, time-dependent conductivity degradation, unpropped fractures, and proppant embedment, can all be considered in the novel fracture-conductivity-calculation methodology. Fracture-Conductivity-Calculation Work Flow The conductivity work flow developed by the authors uses simulated proppant concentration from a fracture model and experimental conductivity measurements of propped and unpropped fractures to define variable conductivity along hydraulic fractures. Conductivity measurements included sets of long-term (50-hour) experimental fracture-conductivity tests with various mesh sizes, proppant types, and closure stresses. The conductivity-calculation work flow developed by the authors was applied to the integrated simulation project of multifractured horizontal wells in the HFTS-2 project. Fig. 1 shows a flow chart of the work flow from fracture propagation modeling through integrated reservoir simulations. In general, the work flow consists of developing a 3D geological model, creating and calibrating parent wells’ hydraulic fracture models, calculating the fracture conductivity based on proppant concentration, history-matching the parent wells’ production and constraints with reservoir simulation, performing fracture-propagation modeling for child wells, and history matching and predicting the estimated ultimate recovery (EUR) for the entire pad. HFTS-2 Project Overview The HFTS-2 is in the Delaware Basin and is a cost-shared, field-based project designed to study hydraulic fracturing processes and production using state-of-the-art diagnostics, including fiber optic (FO) distributed temperature sensing and distributed acoustic sensing (DAS) pressure monitoring, image logs, cores through the fractures, and microseismic monitoring. Many studies have detailed the project overview of the HFTS-2, and this paper’s authors only include the overview of completions in the HFTS-2 project.

A Real-Time Inversion Approach for Fluid-Flow Fractures in Unconventional Stimulated Reservoirs

Summary Fluid-flow fractures, through which fluids can move under pressure, make a more significant contribution to increasing production than do microseismic and propagation fractures. An accurate description of the distribution of fluid-flow fractures is the basis for evaluating hydraulic fracturing and oil/gas recovery. In this study, a real-time inversion approach for fluid-flow fractures was proposed, and the complex fluid-flow fracture morphology was obtained in real time by updating the data of the fracturing construction curve. First, a dynamic permeability model was proposed to describe the filtration rate of the fracturing fluid during hydraulic fracturing. Combined with the point source function, the flowing bottomhole pressure (pwf) can be quickly calculated based on the fracture morphology and displacement of the fracturing fluid. The variance of pwf and bottomhole pressure (pwb) obtained by pump pressure were used as an objective function, and the length of fluid-flow fractures and fracture morphology were used as fitting parameters. The length of the fluid-flow fractures was updated with the simultaneous perturbation stochastic approximation (SPSA) to achieve a rough fitting of the bottomhole pressure. On this basis, a probability function was used to constrain the randomness of the fractures, and the fracture morphology with a fixed fracture length was continuously simulated and finely matched. Finally, a complex fluid-flow fracture morphology was obtained. The method was used to analyze the fluid-flow fracture morphology of multifractured horizontal wells in shale reservoirs, and the fitting rate of the fracturing construction curve was more than 95%. The results show that the total length of the fluid-flow fractures in one stage in naturally fractured reservoirs was approximately 629 m, and those in homogeneous reservoirs and high-stress difference reservoirs were 564 m and 532 m, respectively. The length of fluid-flow fractures with “grooves” in the fracturing construction curve was longer than the length of fluid-flow fractures with “bulges.” The effectively stimulated reservoir area with fluid-flow fractures was only approximately 28–51% of the stimulated reservoir area with microseismic fractures.

A nonlinear mathematical model for fluid flow in low-permeability reservoirs and its effect on well production performance

Fluid flow in low-permeability reservoirs exhibits nonlinear behavior owing to the presence of nano-sized pores and strong fluid-solid interactions. The percolation velocity significantly deviates from that estimated by Darcy's law, particularly in regions with low-pressure gradients. Several modified Darcy's equations have been proposed to describe these nonlinear phenomena. However, existing flow models often suffer from practical limitations, such as complex formulations or challenging determination of parameters. To address this, non-Darcy flow behavior was investigated and a novel low-velocity-nonlinear flow equation that accounts for the boundary layer effect is presented. The curves with different coefficients are plotted and the sensitivity of the corresponding parameters are discussed. Moreover, considering that core flooding experiments fail to accurately reflect actual flow behavior in reservoirs, an inversion method based on genetic algorithm (GA) that utilizes bottomhole pressure (BHP) testing data to estimate the pending coefficients is introduced. Finally, the proposed nonlinear flow equation is applied, using the finite difference method (FDM), to simulate the production performance, including pressure distribution, production rate, and recovery factor of vertical and multi-fractured horizontal wells in low-permeability reservoirs. The results demonstrated significant deviations in reservoir pressure distribution from those estimated using Darcy's law, and the production rate and recovery factor of nonlinear flow were notably lower than those predicted by Darcy's flow law, albeit to varying degrees.

Production prediction based on ASGA-XGBoost in shale gas reservoir

The advancement of horizontal drilling and hydraulic fracturing technologies has led to an increased significance of shale gas as a vital energy source. In the realm of oilfield development decisions, production forecast analysis stands as an essential aspect. Despite numerical simulation being a prevalent method for production prediction, its time-consuming nature is ill-suited for expeditious decision-making in oilfield development. Consequently, we present a data-driven model, ASGA-XGBoost, designed for rapid and precise forecasting of shale gas production from horizontally fractured wells. The central premise of ASGA-XGBoost entails the implementation of ASGA to optimize the hyperparameters of the XGBoost model, thereby enhancing its prediction performance. To assess the feasibility of the ASGA-XGBoost model, we employed a dataset comprising 250 samples, acquired by simulating shale gas multistage fractured horizontal well development through the use of CMG commercial numerical simulation software. Furthermore, XGBoost, GA-XGBoost, and ASGA-XGBoost models were trained using the data from the training set and employed to predict the 30-day cumulative gas production utilizing the data from the testing set. The outcomes demonstrate that the ASGA-XGBoost model yields the lowest mean absolute error and offers optimal performance in predicting the 30-day cumulative gas production. Additionally, the mean absolute error of the unoptimized XGBoost model is markedly greater than that of the optimized XGBoost model, indicating that the latter, refined through the application of intelligent optimization algorithms, exhibits superior performance. The insights gleaned from this investigation have the potential to inform the development of strategic plans for shale gas oilfields, ultimately promoting the cost-effective exploitation of this energy resource.

Interwell fracturing interference evaluation in shale gas reservoirs

Severe fracturing interference in multi-well pads has been identified in shale gas reservoirs. The gas production of affected multi-fractured horizontal wells (MFHWs) decrease a lot and it is hard to restore for most wells even after fracturing fluid flowback. Currently, well interference caused by fracturing operations has become the most important factor affecting the shale gas production. However, the mechanism of fracturing interference and its quantitative impact on gas production in shale gas reservoir are not clear.The aim of this work is to analyze the mechanism and dominated factors of fracturing interference of multi-well pads in shale gas reservoirs, and evaluate the impact of interwell fracturing interference on shale gas production. Firstly, field data in WY Basin are applied to calculate the ratio of impacted wells to newly fractured wells and understand the influencing degree and recovering degree of gas production. The dominated factors of fracturing interference are determined and the interwell fracturing interacting types are presented. Furthermore, the production recovering potential for impacted wells are analyzed. Finally, approaches for mitigating fracturing interference are provided. This work can provide reasonable suggestions for infill well optimization, fracturing design, and interwell fracturing interference mitigation to achieve the highest gas recovery of all multi-well pads in shale gas reservoirs.