A Novel Approach to Reservoir Simulation of Hydraulic Fractures: Performance Improvement Using Pseudo Well Connections

Reservoir simulation is used in most modern reservoir studies to predict future production of oil and gas, and to plan the development of the reservoir. The number of hydraulically fractured wells has risen drastically in recent years due to the increase in production in unconventional reservoirs. Gone are the days of using simple analytic techniques to forecast the production of a hydraulic fracture in a vertical well, and the need to be able to model multiple hydraulic fractures in many stages over long horizontals is now a common practice. The type of simulation approach chosen depends on many factors and is study specific. Pseudo well connection approach was preferred in the current case. Due to the nature of the reservoir simulation problem, a decision needs to be made to determine which hydraulic fracture modeling method might be most suitable for any given study. To do this, a selection of methods is chosen based on what is available at hand, and what is commonly used in various reservoir simulation software packages. The pseudo well connection method, which models hydraulic fractures as uniform conductivity rectangular fractures was utilized for a field of interest referred to as Field A in this paper. Such an assumption of the nature of the hydraulic fracture is common in most modern tools. Field A is a low permeability (0.01md-0.1md), tight (8% to 12% porosity) gas-condensate (API ~51deg and CGR~65 stb/mmscf) reservoir at ~3000m depth. Being structurally complex, it has a large number of erosional features and pinch-outs. The pseudo well connection approach was found to be efficient both terms of replicating data of Field A for a 10 year period while drastically reducing simulation runtime for the subsequent 10 year-period too. It helped the subsurface team to test multiple scenarios in a limited time-frame leading to improved project management.

Optimization of hydraulic fracturing treatment in tight sand reservoir of lower indus basin: an integrated approach

An approach for post-frac production profiling has been presented in this study by integrating a fracture model with a reservoir simulation model for a well drilled in tight sand reservoir of Lower Indus Basin in Pakistan. The presented integrated approach couples the output from the fracture growth model with a reservoir simulation model to effectively predict the behavior of a fractured reservoir. Optimization of hydraulic fracturing was done efficiently through the work presented in this study. The integrated model was used to perform various sensitivities. The production profiles obtained for each case were subsequently used to determine the most profitable case, using an economic model.

The effect of gravity drainage mechanism on oil recovery by reservoir simulation; a case study in an Iranian highly fractured reservoir

A comprehensive reservoir simulation study was performed on an oil field that had a wide fracture network and could be considered a typical example of highly fractured reservoirs in Iran. This field is located in southwest of Iran in Zagros sedimentary basin among several neighborhood fields with relatively considerable fractured networks. In this reservoir, the pressure drops below the saturation pressure and causes the formation of a secondary gas cap. This can help to better assess the gravity drainage phenomenon. We decided to investigate and track the effect of gravity drainage mechanism on the recovery factor of oil production in this field. In this study, after/before the implementation of gas injection scenarios with different discharges, the contribution of gravity drainage mechanism to the recovery factor was found more than 50%. Considering that a relatively large number of studies have been conducted on this field simultaneously with the growth of information from different aspects and this study is the last and most comprehensive study and also the results are extracted from real field data using existing reservoir simulators, it is of special importance and can be used by researchers.

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

Impediments to Refracturing Success in Shale Reservoirs

Stimulations in early horizontal wells in most shale plays are characterized by few and widely spaced perforation clusters, and low amounts of injected fracturing fluid and proppant. Low recovery from these wells has motivated refracturing although outcomes have been interpreted to range from successful to minimal impact based on operator specific evaluations. To tailor available technologies and improve quantification of upsides, there is need for mapping the spatial distribution of remaining resources and developing simpler but reliable analytical techniques. In this study, hydraulic fractures were assumed to be planar in a matrix with low porosity and ultra-low permeability. Consideration of natural fractures and their interaction with stimulation fluids led to addition of distributed fracture networks adjacent to the planar hydraulic fractures to define the composite fracture corridors. A sector model with the aforementioned architecture was used in reservoir simulation to investigate induced temporal and spatial drainage. These findings were used to explain the efficacy of widely used refracturing techniques and how post-refracturing reservoir response can be analyzed. Results from reservoir simulation showed remaining reserves were in the matrix between earlier placed hydraulic fractures aligned along initial perforation clusters, and beyond tips of hydraulic fractures. Upside from refracs could come from creation of new fractures in the matrix between earlier placed fractures and extension of tips of early fractures into virgin matrix. Assessment of these scenarios found the former to be optimal although depletion and existing perforations would limit the stimulation efficiency of new perforations. The second scenario would require large volumes of fracturing fluid to re-initiate fracture propagation. Yet this could trigger interference with offsets or affect drilling and stimulation of planned wells in adjacent acreage. For treatment efficiency, re-casing horizontal wells with competent liners and use of coiled tubing with straddle packers appears a better solution for bypassing old perforations. For the near wellbore and far field, re-stimulating new perforations at low injection rates could allow extension of fractures in virgin matrix surrounded by depleted strata. Real-time surveillance would be essential for mapping flow paths of refracturing fluid. For assessment of refracturing, actual and simulated flow exhibited persistent linear flow (PLF) that could be matched by Arps hyperbolic equation with a b value of 2. Incorporation of a novel fracture geometry factor (FGF) yielded an Arps-based equation that was tested on North American shale refracturing cases that often use post-treatment peak rate and wellhead pressure as measures of success. This study identified factors hindering the success of refracturing and proposed a modified Arps hyperbolic equation to analyze refracturing production data.

Rigorous Performance Evaluation of Stochastic Optimization for Water Injection Strategies

This paper presents an approach to optimize field water injection strategies using stochastic methods under uncertainty. For many fields, voidage replacement was the dictating factor of setting injection strategies. Determining the optimum injection-production ratio (IPR) requires extensive experience taking into consideration all the operational facility constraints. We present the outcome of a study, in which several optimization techniques were used to find the optimum field IPR values and then elaborate on the techniques? strengths and weaknesses. The synthetic reservoir simulation model, with millions of grid blocks and significant numbers of producers and injectors, was divided into seven IPR regions based on a streamline study. Each region was assigned an IPR value with an associated uncertainty interval. An ensemble of fifty probabilistic scenarios was generated by experimental design, using Latin Hypercube sampling of IPR values within tolerance limits. Scenarios were used as the main sampling domain to evaluate a family of optimization engines: population-based methods of artificial intelligence (AI), such as Genetic algorithms and Evolutionary strategies, Bayesian inference using sequential or Markov chain Monte Carlo, and proxy-based optimization. The optimizers were evaluated based on the recommended IPR values that meet the objective of minimizing the water cut by maximizing oil production and minimizing water production. The speed of convergence of the optimization process was also a subject of evaluation. To ensure unbiased sampling of IPR values and to prevent oversampling of boundary extremes, a uniform triangular distribution was designed. The results of the study show a clear improvement of the objective function, compared to the initial sampled cases. As a direct search method, the Evolutionary strategies with covariance matrix adaptation (ES-CMA) yielded the optimum IPR value per region. While examining the effect of applying these IPR values in the reservoir simulation model, a significant reduction of water production from the initial cases without an impact on the oil production was observed. Compared to ESCMA, other optimization methods have dem

Long Term Production Strategy - Application of a Dynamically Integrated Reservoir and Production Model to Identify Compression Requirements and to Address Production Deferral in a Giant Gas Field

There are several operational challenges associated with a gas field producing in recycle or depletion mode, including a reasonable forecast and a robust production strategy planning. The complex reservoir dynamics further demands faster and reasonable analysis and decision-making. This paper discusses an all-inclusive integrated modeling approach to devise a production strategy incorporating the detailed compressor design requirements to ensure that a consistent production-stream is available in the long-term considering technical and economic aspects. The proposed production strategy is a two-fold approach. In the first step, the process utilizes the current reservoir simulation data in the production-forecast model. This history matched model captures the reservoir dynamics such as reservoir pressure decline and accounts for future wells drilling-requirements. However, the detailed production hydraulics in wellbore and surface facilities is not captured in the model. Further, to consider the declining well-performance and facility bottlenecks, integrated analysis is required. So, in the second step, the reservoir simulation model is dynamically integrated to take the input from the production model, encompassing detailed well and surface facility digital twins. The continuous interaction provides a highly reliable production profile that can be used to produce a production strategy of compressor design for the future. A strong interactive user-interface in the digital platform enables the user to configure various what-if scenarios efficiently, considering all anticipated future events and production conditions. The major output of the process was the accurate identification of the pressure-profile at multiple surface facility locations over the course of the production. Using the business-plan, field development strategy, production-profile, and the reservoir simulation output, reliable pressure-profiles were obtained, giving an indication of the declining pressures at gathering manifold over time. A well level production-profile-forecast helped in prioritizing wells for rerouting as well as workover requirements. As an outcome of this study, several manifolds were identified that are susceptible to high-pressure decline caused by declining reservoir pressures. To capture this pressure decline, a compressor mechanism was put in place to transfer the fluid to its delivery point. As this study utilizes several timesteps for the production forecast estimation, flexible routine options are also provided to the engineers to ensure that backpressure is minimized to avoid a larger back pressure on the wells for quick gains. This solution improves the efficiency of the previous approaches that were entirely relying on the reservoir simulation model to capture the pressure decline at the wellhead to forecast the compressor needs. In this methodology, the pressure profile at each node was captured to simulate a real production scenario. This holistic approach is in line with Operator's business plan strategy to identify the needs of external energy-source to avoid production-deferral.

Multi-Fidelity Bayesian Approach for History Matching in Reservoir Simulation

History matching is a critical step within the reservoir management process to synchronize the simulation model with the production data. The history-matched model can be used for planning optimum field development and performing optimization and uncertainty quantifications. We present a novel history matching workflow based on a Bayesian framework that accommodates subsurface uncertainties. Our workflow involves three different model resolutions within the Bayesian framework: 1) a coarse low-fidelity model to update the prior range, 2) a fine low-fidelity model to represent the high-fidelity model, and 3) a high-fidelity model to re-construct the real response. The low-fidelity model is constructed by a multivariate polynomial function, while the high-fidelity model is based on the reservoir simulation model. We firstly develop a coarse low-fidelity model using a two-level Design of Experiment (DoE), which aims to provide a better prior. We secondly use Latin Hypercube Sampling (LHS) to construct the fine low-fidelity model to be deployed in the Bayesian runs, where we use the Metropolis-Hastings algorithm. Finally, the posterior is fed into the high-fidelity model to evaluate the matching quality. This work demonstrates the importance of including uncertainties in history matching. Bayesian provides a robust framework to allow uncertainty quantification within the reservoir history matching. Under uniform prior, the convergence of the Bayesian is very sensitive to the parameter ranges. When the solution is far from the mean of the parameter ranges, the Bayesian introduces bios and deviates from the observed data. Our results show that updating the prior from the coarse low-fidelity model accelerates the Bayesian convergence and improves the matching convergence. Bayesian requires a huge number of runs to produce an accurate posterior. Running the high-fidelity model multiple times is expensive. Our workflow tackles this problem by deploying a fine low-fidelity model to represent the high-fidelity model in the main runs. This fine low-fidelity model is fast to run, while it honors the physics and accuracy of the high-fidelity model. We also use ANOVA sensitivity analysis to measure the importance of each parameter. The ranking gives awareness to the significant ones that may contribute to the matching accuracy. We demonstrate our workflow for a geothermal reservoir with static and operational uncertainties. Our workflow produces accurate matching of thermal recovery factor and produced-enthalpy rate with physically-consistent posteriors. We present a novel workflow to account for uncertainty in reservoir history matching involving multi-resolution interaction. The proposed method is generic and can be readily applied within existing history-matching workflows in reservoir simulation.

Coupled Effect of Imbibition Capillary Pressure and Matrix-Fracture Transfer on Oil Recovery from Dual-Permeability Reservoirs

This paper presents a numerical study to examine how the interplay between the matrix imbibition capillary pressure (Pci) and matrix-fracture transfer affects oil recovery from naturally-fractured reservoirs under waterflooding. We use a dual-porosity, dual-permeability (DPDP) finite difference simulator to investigate the impact of uncertainties in Pci on the waterflood recovery behavior and matrix-fracture transfer. A comprehensive assessment of the factors that control the matrix-fracture transfer, namely Pci, gravity forces, shape factor and fracture-matrix permeabilities is presented. We examine how the use of Pci curves in reservoir simulation can affect the recovery assessment. We present two conceptual scenarios to demonstrate the impact of spontaneous and forced imbibition on the flood-front movement, waterflood recovery processes, and ultimate recovery in the DPDP reservoir systems of varying reservoir quality. The results demonstrate that the inclusion of Pci in reservoir simulation delays the breakthrough time due to a higher displacement efficiency. The study reveals that the matrix-fracture transfer is mainly controlled by the fracture surface area, fracture permeability, shape factor, and the uncertainty in Pci. We underline a discrepancy among various shape factors proposed in the literature due to three main factors: (1) the variations in matrix-block geometries considered, (2) how the physics of imbibition forces that control the multiphase fluid transfer is captured, and (3) how the assumption of pseudo steady-state flow is addressed.