Development of an Artificial Neural Network for Cyclic Steam Stimulation Method in Naturally Fractured Reservoirs PDF Download
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Author: Buket Arpaci
Publisher:
ISBN:
Category :
Languages : en
Pages : 171
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Author: Buket Arpaci
Publisher:
ISBN:
Category :
Languages : en
Pages : 171
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Author: Ahmet Ersahin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Due to a sharp fall in oil prices in late 2014, many oil exploration companies have either stopped operations or postponed projects to a future date. The resulting slowdown has strengthened the dependency on existing developed fields for oil production. This is a cause of concern for major oil corporations and governments worldwide, as the dependence on mature fields suggests that conventional oil extraction techniques may not be enough to maintain current demand and may lead to significant profit losses. Thus, the development of enhanced oil recovery (EOR) (also known as tertiary recovery) methods to improve recovery from developed fields has attracted attention.Thermal recovery, a widely used EOR method in heavy oil reservoirs, involves the introduction of heat into the formation to reduce the viscosity of the oil in the reservoir. Cyclic steam stimulation (CSS) is an effective thermal process used with naturally fractured reservoirs. The cyclic steam injection (CSI) method incorporates the stages of injecting, soaking and production one by one in a single well.The use of a commercial simulator for estimating production is common. However, the process can be time consuming and complex. Alternatively, it is possible to achieve results within seconds using an adequately trained artificial neural network (ANN).This study analyzes CSI performance based on its effectiveness with respect to viscosity contours and cumulative oil production. Naturally fractured reservoirs are excellent targets for steam injection because they possess a structure where steam can easily diffuse.
Author: Santosh Phani Bhushan Chintalapati
Publisher:
ISBN:
Category :
Languages : en
Pages : 150
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Author: Mohammed Alghazal
Publisher:
ISBN:
Category :
Languages : en
Pages :
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The increasing demand for energy and accelerated consumption of hydrocarbon fuel have made it a necessary objective for the oil and gas industry to continuously search for ways to improve and maximize recovery from oil reservoirs, to meet this growing global demand. Water flooding is one of the most common secondary recovery practices used in the petroleum industry to maintain reservoir pressure and improve oil displacement efficiency and recovery. Nonetheless, water flooding could pose several production problems in certain types of naturally fractured reservoirs, jeopardizing the overall sweep efficiency and oil recovery in the field. The presence of these heterogeneous natural fracture systems highly influences and complicates fluid flow process in the reservoir's transport media. These fractures provide easy conduits and fluid pathways for the injected water, causing early premature water breakthrough, excessive water production and rapid decline of oil rate.The implementation of polymer gel treatments is one of the viable solution commonly used in the industry to mitigate sweep conformance problems and improve oil recovery from naturally fractured reservoirs. Water-soluble polymer solutions are combined with cross-linking agents to form an in-situ gel that can be injected with water into the reservoir media. This polymer gel not only improves the overall mobility ratio of injected fluid, but also provides a mean to plug the conduit fractures and subsequently improving overall volumetric sweep efficiency and oil recovery from the reservoir matrix.Reservoir simulators are commonly used to build reliable reservoir models for the purpose of history matching, production forecasting and evaluation of various design scenarios. Nonetheless, reservoir simulation can become very computationally demanding and time- consuming process. This problem could be overcome by the development of Artificial Neural Network (ANN) models that could be used to generate various possible scenarios at a muchefficient time pace compared to reservoir simulation.The main objective of this research is to develop neuro-simulation proxy models for theimplementation of water flooding and polymer gel flooding in naturally fractured reservoirs. Three main ANN models, one forward and two inverses, were developed for each scenario, water flooding and polymer gel flooding.The first ANN, Forward ANN, provides a forward solution to predict the production profiles of oil rate, water cut and recovery factor for a given set of reservoir and design data. Forward results were matched within a desired tolerance of l0%. The second ANN, Inverse ANN- 1, provides an inverse-looking solution to estimate the project design parameters required to produce a given production profile for a given set of reservoir properties. Five design parameters were investigated, including: reservoir's drainage area, injection rate, producer bottom-hole pressure, polymer concentration and cross linker concentration. The last ANN, Inverse ANN-2, can be used as a tool for history matching and estimation of reservoir properties given a production profile and project design parameters. The reservoir properties predicted by this model include: matrix and fracture porosity, matrix and fracture permeability, fracture spacing, reservoir thickness and initial water saturation. The results from inverse ANN models were produced with an average error of 5 to 10%, per design parameter, and an average error of 8 to 28%, per reservoir property. Collectively, a total of six ANN tools were developed for the purpose of this research and were all encapsulated in a user-friendly Graphical User Interface (GUI) environment, to allow the end users for an easy access and utilization of these expert tools.
Author: Ayhan Sengel
Publisher:
ISBN:
Category :
Languages : en
Pages : 142
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Author: Hussain Gaw
Publisher:
ISBN:
Category :
Languages : en
Pages :
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In order to meet the increasing demand for natural gas, it has become important to increase production. Drilling horizontal wells in naturally fractured gas reservoirs can greatly help in achieving the desired high gas production. Reservoir simulation is used to history match production profiles in order to predict important reservoir characteristics. Nevertheless, the use of commercial simulators is time consuming. Therefore, the search for alternatively fast means to predict reservoir properties promoted the use of artificial neural networks. These networks have grown in popularity because of their ability to solve non-linear relationship problems, generate accurate analysis and predict results from large number of historical data. Artificial neural network (ANN) is a mathematical model, which tries to mimic the structure and functionality of a human biological network in acquiring, storing and using experimental knowledge. ANN predicts target outputs when given a set of input and it can be trained until optimum results are reached. The main objective of this study is to develop artificial neural networks that will perform pressure and rate transient analysis for dry, wet and condensate gas reservoirs with fixed composition. The network is given three main variables: production profiles, well parameters and reservoir characteristics, where each variable can be predicted in the presence of the other two. Three separate artificial neural networks were trained for each of the three models. These artificial neural network showed good results and were able to predict the desired outputs with an average error less than 10%.
Author: Shahab Mohaghegh
Publisher: CRC Press
ISBN: 1315280809
Category : Science
Languages : en
Pages : 282
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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: Chris Raymond Affane Nguema
Publisher:
ISBN:
Category :
Languages : en
Pages :
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The increasing demand in energy has strengthen the dependence on fossil fuels. On the other hand, the conventional hydrocarbon reservoirs are depleting rather quickly which prompted an important study of the hydrocarbon reservoirs from unconventional reservoirs. Over the last two decades, the improvement in technology and recovery methods has allowed the industry to extract hydrocarbon from unconventional reservoirs. There are been important advancements in drilling and reservoir engineering technologies.In order to overcome some of the costs associated with the exploitation of those reservoirs, an extensive use of techniques such as directional drilling to has been largely recommended and has proven to be more efficient. Directional drilling allows to control the direction of the wellbore to increase the contact with the target or pay zone location among other significant benefits.Reservoir simulation refers to constructing computer models to gain a better understanding of reservoirs. It is mostly used to predict the flow of fluids or to match the properties of the reservoir. However, it has shown to have be limited when not enough information about the reservoir is available.Artificial neural network (ANN) is a technique used in many fields that has been able to compensate for some of the limitations associated with other approaches such as reservoir simulation. It relies on observed data to build highly non-linear and strong links among them that make it possible to obtain a more accurate prediction of the missing information.The main goal of this study is to develop an ANN tool for a single phase unconventional gas reservoir that can predict reservoir properties such as porosity, permeability and compressibility. The tool applicability has been extended for a large range of data. It provides predictions from two network structures, cascade forward backpropagation and radial basis function with an option to compare them. Each of the ANN model, therefore, differs by the type of networks used, the porosity system (single or dual), the well inclination (0 to 90), and the transient data available (pressure or production rate). The performance of each network was evaluated using the average percent error, the mean bias error (MBE), and the root mean square error (RMSE).
Author: F. Emre Artun
Publisher:
ISBN:
Category :
Languages : en
Pages : 410
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Author:
Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 896
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