Optimized Design of Cyclic Pressure Pulsing in Naturally Fractured Reservoirs Using Neural-network Based Proxy Models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Optimized Design of Cyclic Pressure Pulsing in Naturally Fractured Reservoirs Using Neural-network Based Proxy Models PDF full book. Access full book title Optimized Design of Cyclic Pressure Pulsing in Naturally Fractured Reservoirs Using Neural-network Based Proxy Models by F. Emre Artun. Download full books in PDF and EPUB format.
Author: F. Emre Artun
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: F. Emre Artun
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author:
Publisher: ScholarlyEditions
ISBN: 1464966672
Category : Technology & Engineering
Languages : en
Pages : 434
Get Book Here
Book Description
Issues in Fossil Fuel Energy Technologies / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Fossil Fuel Energy Technologies. The editors have built Issues in Fossil Fuel Energy Technologies: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Fossil Fuel Energy Technologies in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Fossil Fuel Energy Technologies: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
Author: Shahab Mohaghegh
Publisher: CRC Press
ISBN: 1315280809
Category : Science
Languages : en
Pages : 282
Get Book Here
Book Description
Data-driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO2 require the development of numerical reservoir simulation models. This book is the first to examine the contribution of artificial intelligence and machine learning in data-driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of artificial intelligence and machine learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects.
Author: Chuxi Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 284
Get Book Here
Book Description
Given the dynamic production data of a reservoir, numerical optimization tools such as history matching can minimize the global error and find an optimal reservoir model that can approximate the fracture geometry and petrophysical parameters in the subsurface. For unconventional reservoirs, the idea behind the automatic history matching is well developed and the workflow is also applied to statistically generate an ensemble of solutions that quantitatively characterizes associated uncertainties. However, more uncertainties regarding fracture and reservoir properties could be further reduced by using available information. Therefore, the objective of this study is to minimize uncertainty when make realizations of shale reservoirs, by integration of data from geology and geomechanics. We utilized the developed automatic history matching (AHM) code and modified the proxy engine, by substituting the neural network (NN) model with XGBoost (XGBOOST) model. The XGBOOST is found to perform more efficiently and accurately than NN, when the size of the available dataset for training is small. Furthermore, the AHM workflow is capable of modelling non-uniform half-length of hydraulic fractures in the corner point gridding system and complex, realistic natural fracture distributions using the fractal theory. Both of these functionalities partially fulfill some degrees of reality, by mimicking the irregular half-length outputted from fracture modelling software and naturally occurring patterns often found at cores. We applied this innovative approach to actual shale gas and shale oil wells. We then found that by coupling additional data into the AHM process, the fracture geometries and petrophysical properties can be more accurately depicted. The obtained results are also highly assimilating with the field experience from the engineers. In addition, by studying natural fractures in the model, we found out that the connectivity between natural fractures and wellbore/hydraulic fractures plays an important role in determining the well’s EUR potential. This study is beneficial because more reliable and robust results based on geological/geomechanical information, along with non-deterministic realizations of reservoir and fractures, can provide invaluable guidance towards well spacing planning, EUR estimation and economic appraisal, and fracture design optimizations
Author: Zhenzihao Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Significant amounts of oil and gas are trapped in naturally fractured reservoirs, a phenomenon which has attracted growing attention as the shale boom has evolved. The dual-porosity concept has been commonly used in modeling these naturally fractured reservoirs. In this model, the fluid flows through the fracture system in the reservoir, while matrix blocks are segregated by the fractures and act as the fluids sources for them. This model was originally developed for liquid in naturally fractured systems and therefore inadequate for capturing pressure-dependent effects in gas systems. This study presents a rigorous derivation of a gas interporosity flow equation that accounts for the effects of pressure-sensitive properties. A numerical simulator using the gas interporosity flow equation is built and demonstrates a significant difference in system response from that of a simulator implementing a liquid-form interporosity flow equation. For this reason, rigorous modeling of interporosity flow is considered essential to decline curve analysis for naturally fractured gas reservoirs. State-of-the-art approaches to decline curve analysis have typically used pseudo-functions, yet these approaches remain limited in utility as demonstrated in many previous comparisons between analytical results and production data that revealed discrepancy. In this study, we show the gas interporosity flow equation eliminates the discrepancy at the decline stage and enables rigorous decline curve analysis for production at constant bottomhole pressure. We investigate the applicability of a density-based approach for decline curve analysis for production at constant bottomhole pressure in dual-porosity gas systems. This approach relates gas production profiles to their liquid counterparts by decoupling pressure-dependent effects from pressure depletion. This study further demonstrates the process of rigorous derivation for density-based decline curve analysis in dual-porosity gas systems. The interporosity flow equation for gas is used, and a deliverability equation for dual-porosity systems is rigorously derived in the process.In light of density-based approach for production at constant bottomhole pressure in dual-porosity gas systems, a density-based, rescaled exponential model for variable pressure drawdown/variable rate production was developed for dual-porosity gas systems. We also explore straight-line analysis for convenient prediction of OGIP and production rate at variable pressure drawdown/rate production. This density-based model was tested in a variety of scenarios to showcase its validity. Furthermore, based on Warren and Root's model, a density-based exponential model for variable pressure drawdown/rate in dual-porosity liquid systems is proposed and verified. Then, a straight-line analysis is proposed to enable explicit OOIP prediction and convenient future production calculation. Aside from these, we develop a double-exponential decline model under constant BHP for liquid which is not only applicable to both decline stages but also convenient to implement.
Author: Sutthaporn Tripoppoom
Publisher:
ISBN:
Category :
Languages : en
Pages : 448
Get Book Here
Book Description
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
Author: Jia Lu
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
In naturally fractured reservoirs, reservoir characterization is critical to the production of hydrocarbon, including but not limited to porosity, permeability, pay zone thickness and fracture spacing. Laboratory measurements, well-logging technique, and mathematical models are three major characterization approaches that are widely used to determine and analyze the reservoir characterization and production profiles. Amongst these approaches, mathematical models are commonly used as estimation tools. The purpose of this thesis is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software1. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats.
Author: Sutthaporn Tripoppoom
Publisher: Elsevier
ISBN: 0128222425
Category : Science
Languages : en
Pages : 288
Get Book Here
Book Description
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
Author: Hugo Araujo N.
Publisher:
ISBN:
Category : Carbonate reservoirs
Languages : en
Pages : 656
Get Book Here
Book Description
Author: Mishal Habis Al Harbi
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Streamline-based models have shown great potential in reconciling high resolution geologic models to production data. In this work we extend the streamline-based production data integration technique to naturally fractured reservoirs. We use a dual-porosity streamline model for fracture flow simulation by treating the fracture and matrix as separate continua that are connected through a transfer function. Next, we analyticallycompute the sensitivities that define the relationship between the reservoir properties and the production response in fractured reservoirs. Finally, production data integration is carried out via the Generalized Travel Time inversion (GTT). We also apply the streamline-derived sensitivities in conjunction with a dual porosity finite difference simulator to combine the efficiency of the streamline approach with the versatility of the finite difference approach. This significantly broadens the applicability of the streamline- based approach in terms of incorporating compressibility effects and complex physics. The number of reservoir parameters to be estimated is commonly orders of magnitude larger than the observation data, leading to non-uniqueness and uncertainty in reservoir parameter estimate. Such uncertainty is passed to reservoir response forecast which needs to be quantified in economic and operational risk analysis. In this work we sample parameter uncertainty using a new two-stage Markov Chain Monte Carlo (MCMC) that is very fast and overcomes much of its current limitations. The computational efficiency comes through a substantial increase in the acceptance rate during MCMC by using a fast linearized approximation to the flow simulation and the likelihood function, the critical link between the reservoir model and production data. The Gradual Deformation Method (GDM) provides a useful framework to preserve geologic structure. Current dynamic data integration methods using GDM are inefficient due to the use of numerical sensitivity calculations which limits the method to deforming two or three models at a time. In this work, we derived streamline-based analytical sensitivities for the GDM that can be obtained from a single simulation run for any number of basis models. The new Generalized Travel Time GDM (GTT-GDM) is highly efficient and achieved a performance close to regular GTT inversion while preserving the geologic structure.
