Assisted History Matching for Unconventional Reservoirs Books

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Assisted History Matching for Unconventional Reservoirs


Assisted History Matching for Unconventional Reservoirs
  • Author : Sutthaporn Tripoppoom
  • Publisher : Gulf Professional Publishing
  • Release : 2021-08-15
  • ISBN : 0128222425
  • Language : En, Es, Fr & De
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As unconventional reservoir activity grows in demand, reservoir engineers relying on history matching are challenged with this time-consuming task in order to characterize hydraulic fracture and reservoir properties, which are expensive and difficult to obtain. Assisted History Matching for Unconventional Reservoirs delivers a critical tool for today's engineers proposing an Assisted History Matching (AHM) workflow. The AHM workflow has benefits of quantifying uncertainty without bias or being trapped in any local minima and this reference helps the engineer integrate an efficient and non-intrusive model for fractures that work with any commercial simulator. Additional benefits include various applications of field case studies such as the Marcellus shale play and visuals on the advantages and disadvantages of alternative models. Rounding out with additional references for deeper learning, Assisted History Matching for Unconventional Reservoirs gives reservoir engineers a holistic view on how to model today's fractures and unconventional reservoirs. Provides understanding on simulations for hydraulic fractures, natural fractures, and shale reservoirs using embedded discrete fracture model (EDFM) Reviews automatic and assisted history matching algorithms including visuals on advantages and limitations of each model Captures data on uncertainties of fractures and reservoir properties for better probabilistic production forecasting and well placement

Shale Gas and Tight Oil Reservoir Simulation


Shale Gas and Tight Oil Reservoir Simulation
  • Author : Wei Yu
  • Publisher : Gulf Professional Publishing
  • Release : 2018-11
  • ISBN : 0128138688
  • Language : En, Es, Fr & De
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Shale Gas and Tight Oil Reservoir Simulation delivers the latest research and applications used to better manage and interpret simulating production from shale gas and tight oil reservoirs. Starting with basic fundamentals, the book then includes real field data that will not only generate reliable reserve estimation, but also predict the effective range of reservoir and fracture properties through multiple history matching solutions. Also included are new insights into the numerical modelling of CO2 injection for enhanced oil recovery in tight oil reservoirs. This information is critical for a better understanding of the impacts of key reservoir properties and complex fractures. Rounding out with geomechanics modelling and data mining, this book helps reservoir and petroleum engineers gain a better understanding that can be applied to unconventional reservoirs. Models the well performance of shale gas and tight oil reservoirs with complex fracture geometries Teaches how to perform sensitivity studies, history matching, production forecasts, and economic optimization for shale-gas and tight-oil reservoirs Helps readers investigate data mining techniques, including the introduction of nonparametric smoothing models

Assisted History Matching Workflow for Unconventional Reservoirs


Assisted History Matching Workflow for Unconventional Reservoirs
  • Author : Sutthaporn Tripoppoom
  • Publisher :
  • Release : 2019
  • ISBN : OCLC:1107324151
  • Language : En, Es, Fr & De
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The information of fractures geometry and reservoir properties can be retrieved from the production data, which is always available at no additional cost. However, in unconventional reservoirs, it is insufficient to obtain only one realization because the non-uniqueness of history matching and subsurface uncertainties cannot be captured. Therefore, the objective of this study is to obtain multiple realizations in shale reservoirs by adopting Assisted History Matching (AHM). We used multiple proxy-based Markov Chain Monte Carlo (MCMC) algorithm and Embedded Discrete Fracture Model (EDFM) to perform AHM. The reason is that MCMC has benefits of quantifying uncertainty without bias or being trapped in any local minima. Also, using MCMC with proxy model unlocks the limitation of an infeasible number of simulations required by a traditional MCMC algorithm. For fractures modeling, EDFM can mimic fractures flow behavior with a higher computational efficiency than a traditional local grid refinement (LGR) method and more accuracy than the continuum approach. We applied the AHM workflow to actual shale gas wells. We found that the algorithm can find multiple history matching solutions and quantify the fractures and reservoir properties posterior distributions. Then, we predicted the production probabilistically. Moreover, we investigated the performance of neural network (NN) and k-nearest neighbors (KNN) as a proxy model in the proxy-based MCMC algorithm. We found that NN performed better in term of accuracy than KNN but NN required twice running time of KNN. Lastly, we studied the effect of enhanced permeability area (EPA) and natural fractures existence on the history matching solutions and production forecast. We concluded that we would over-predict fracture geometries and properties and estimated ultimate recovery (EUR) if we assumed no EPA or no natural fractures even though they actually existed. The degree of over-prediction depends on fractures and reservoir properties, EPA and natural fractures properties, which can only be quantified after performing AHM. The benefits from this study are that we can characterize fractures geometry, reservoir properties, and natural fractures in a probabilistic manner. These multiple realizations can be further used for a probabilistic production forecast, future fracturing design improvement, and infill well placement decision

Uncertainty Quantification of Unconventional Reservoirs Using Assisted History Matching Methods


Uncertainty Quantification of Unconventional Reservoirs Using Assisted History Matching Methods
  • Author : Esmail Mohamed Khalil Eltahan
  • Publisher :
  • Release : 2019
  • ISBN : OCLC:1153169644
  • Language : En, Es, Fr & De
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A hallmark of unconventional reservoirs is characterization uncertainty. Assisted History Matching (AHM) methods provide attractive means for uncertainty quantification (UQ), because they yield an ensemble of qualifying models instead of a single candidate. Here we integrate embedded discrete fracture model (EDFM), one of fractured-reservoirs modeling techniques, with a commercial AHM and optimization tool. We develop a new parameterization scheme that allows for altering individual properties of multiple wells or fracture groups. The reservoir is divided into three types of regions: formation matrix; EDFM fracture groups; and stimulated rock volume (SRV) around fracture groups. The method is developed in a sleek, stand-alone form and is composed of four main steps: (1) reading parameters exported by tool; (2) generating an EDFM instance; (3) running the instance on a simulator; and (4) calculating a pre-defined objective function. We present two applications. First, we test the method on a hypothetical case with synthetic production data from two wells. Using 20 history-matching parameters, we compare the performance of five AHM algorithms. Two of which are based on Bayesian approach, two are stochastic particle-swarm optimization (PSO), and one is commercial DECE algorithm. Performance is measured with metrics, such as solutions sample size, total simulation runs, marginal parameter posterior distributions, and distributions of estimated ultimate recovery (EUR). In the second application, we assess the effect of natural fractures on UQ of a single horizontal well in the middle Bakken. This is achieved by comparing four AHM scenarios with increasingly varying natural-fracture intensity. Results of the first study show that, based on pre-set acceptance criteria, DECE fails to generate any satisfying solutions. Bayesian methods are noticeably superior to PSO, although PSO is capable to generate large number of solutions. PSO tends to be focused on narrow regions of the posteriors and seems to significantly underestimate uncertainty. Bayesian Algorithm I, a method with a proxy-based acceptance/rejection sampler, ranks first in efficiency but evidently underperforms in accuracy. Results from the second study reveal that, even though varying intensity of natural fractures cam significantly alter other model parameters, that appears not to have influence on UQ (or long-term production)

Intelligent Digital Oil and Gas Fields


Intelligent Digital Oil and Gas Fields
  • Author : Gustavo Carvajal
  • Publisher : Gulf Professional Publishing
  • Release : 2017-12-14
  • ISBN : 9780128047477
  • Language : En, Es, Fr & De
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Intelligent Digital Oil and Gas Fields: Concepts, Collaboration, and Right-time Decisions delivers to the reader a roadmap through the fast-paced changes in the digital oil field landscape of technology in the form of new sensors, well mechanics such as downhole valves, data analytics and models for dealing with a barrage of data, and changes in the way professionals collaborate on decisions. The book introduces the new age of digital oil and gas technology and process components and provides a backdrop to the value and experience industry has achieved from these in the last few years. The book then takes the reader on a journey first at a well level through instrumentation and measurement for real-time data acquisition, and then provides practical information on analytics on the real-time data. Artificial intelligence techniques provide insights from the data. The road then travels to the "integrated asset" by detailing how companies utilize Integrated Asset Models to manage assets (reservoirs) within DOF context. From model to practice, new ways to operate smart wells enable optimizing the asset. Intelligent Digital Oil and Gas Fields is packed with examples and lessons learned from various case studies and provides extensive references for further reading and a final chapter on the "next generation digital oil field," e.g., cloud computing, big data analytics and advances in nanotechnology. This book is a reference that can help managers, engineers, operations, and IT experts understand specifics on how to filter data to create useful information, address analytics, and link workflows across the production value chain enabling teams to make better decisions with a higher degree of certainty and reduced risk. Covers multiple examples and lessons learned from a variety of reservoirs from around the world and production situations Includes techniques on change management and collaboration Delivers real and readily applicable knowledge on technical equipment, workflows and data challenges such as acquisition and quality control that make up the digital oil and gas field solutions of today Describes collaborative systems and ways of working and how companies are transitioning work force to use the technology and making more optimal decisions