Author: Jacob Cox
Publisher:
ISBN:
Category :
Languages : en
Pages :
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The goal of this study was to create a tool with the use of an artificial neural network (ANN) that could quickly predict the inverse solution to pressure transient (PT) data created for multiple multi-lateral wells completed within a tight-gas dual-porosity reservoir. This inverse tool would be able to predict the user's reservoir parameters nearly instantaneously with known inputs of PT data and wellbore design. This tool will take ideas from current well test analysis to aid in the training of the neural network. However, once the network has been trained, it will be able to predict multiple key reservoir properties and the time consuming process of conventional well test analysis will no longer be an issue.
Author: Mohammad Nasser Alajmi
Publisher:
ISBN:
Category : Hydrocarbon reservoirs
Languages : en
Pages : 204
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Author: Kittikorn Khattirat
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 164
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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: Jian Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Traditional rate and pressure transient analysis apply simplified analytical models to calculate the reservoir characteristics. The analytical models are based on ideal assumptions which are hardly satisfied in practice, and the analysis process also relies on well-trained human experts and their valuable experiences. The traditional rate and pressure transient analysis approaches demand extensive resources in terms of personnel and time. Instead of relying on analytical models and human experts, Artificial Neural Network (ANN) tools, as an analog of the neural systems in human brains, are developed for rate and pressure transient analysis. The ANN tools developed are proved successful in processing complex problems with much less cost. Different from analytical models, however, each ANN tool developed is only applicable in analyzing problems of certain types of reservoir and well conditions. If the problem is not within the specification or range of the parameters of the existing ANN tools, a new ANN tool is required to be developed from the scratch. The development process of the ANN tools is called the Neuro-Simulation protocol. It applies numerical reservoir simulators to generate randomly distributed data sets and train an ANN to analyze the transient data. This protocol relies on well trained and experienced researchers and commercial numerical reservoir simulator and ANN development software. In this study, an automated Neuro-Simulation protocol is developed to assist and accelerate the development process of ANN tools for rate and pressure transient analysis. The protocol has the capability of automating the major processes in the Neuro-Simulation protocol. The protocol is implemented into a comprehensive toolkit. In this toolkit, a comprehensive and generalized in-house numerical reservoir simulator is implemented. The rectangular and radial-cylindrical grid systems are implemented based on the framework, and the black oil, compositional and naturally fractured system fluid flow models are developed into the simulator. An ANN development tool is developed with multiple built-in activation functions and learning algorithms. The automated Neuro-Simulation protocol based on the communication and cooperation between the in-house numerical reservoir simulator and ANN development tool is established. The toolkit also contains a user-friendly GUI and mini PC to provide convenience. The toolkit is designed to be general, flexible and independent. A tight gas reservoir system with dual-lateral horizontal well is studied to illustrate the capability of the protocol and toolkit.
Author: Nilsu Kistak
Publisher:
ISBN:
Category :
Languages : en
Pages : 114
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Author: Jafar M-amin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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The Artificial Neural Network has been used in a variety of complex conditions where the non-linear relationships between existing variables can be difficult to understand. Forecasting the reservoir production along with resembling the reservoir behavior are the main goals of using simulators. To reduce the number of runs by a simulator, the Artificial Neural Network is used to predict the relationship between variables using a history matching technique. The Artificial Neural Network (ANN) mainly consists of two solution parts. The first part is called forward solution and is used to obtain production properties from parameters generated using the simulator. Using an inverse solution enables users to obtain reservoir or well design data based on production data such as production rate and cumulative production. The artificial neurons are mathematical functions that handle multiplying, summing and activating the neurons to be transformed to a desired goal, an output. Different scenarios have been implemented following the Artificial Neural Network application to the petroleum industry. As the number of layers in a multilayer reservoir increases, neural network requires more time and computations. By using optimization methods and applying them to the ANN, the tool decides on the best design in which the least error is achieved. The developed tool has been assessed based on the comparison of simulator parameters and ANN learned variables.To enhance the utilization of the tool, a graphic user interface (GUI) has been created to help users access the data and results efficiently. The GUI is itself considered to be an auxiliary tool and based on the data provided as inputs, results would be available.
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: Srinivasa Lingireddy
Publisher: ASCE Publications
ISBN: 9780784475607
Category : Technology & Engineering
Languages : en
Pages : 196
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Book Description
Prepared by the Water Supply Engineering Technical Committee of the Infrastructure Council of the Environmental and Water Resources Institute of ASCE. This report examines the application of artificial neural network (ANN) technology to water supply engineering problems. Although ANN has rarely been used in in this area, those who have done so report findings that were beyond the capability of traditional statistical and mathematical modeling tools. This report describes the availability of diverse applications, along with the basics of neural network modeling, and summarizes the experiences of groups of researchers around the world who successfully demonstrated significant benefits from using ANN technology in water supply engineering. Topics include: Forecasting salinity levels in River Murray, South Australia; Predicting gastroenteritis rates and waterborne outbreaks; Modeling pH levels in a eutrophic Middle Loire River, France; and ANNs as function approximation tools replacing rigorous mathematical simulation models for analyzing water distribution networks.
Author:
Publisher:
ISBN:
Category : Petroleum
Languages : en
Pages : 996
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