Optimization of well placement geothermal reservoirs using artificial intelligence

This research proposes a framework for determining the optimum location of an injection well using an inference method, artificial neural networks and a search algorithm to create a search space and locate the global maxima. A complex carbonate geothermal reservoir (Kizildere Geothermal field, Turkey) production history is used to evaluate the proposed framework. Neural networks are used as a tool to replicate the behavior of commercial simulators, by capturing the response of the field given a limited number of parameters such as temperature, pressure, injection location, and injection flow rate. A study on different network designs indicates that a combination of neural network and an optimization algorithm (explicit search with variable stepping) to capture local maxima can be used to locate a region or a location for optimum well placement. Results also indicate shortcomings and possible pitfalls associated with the approach. With the provided flexibility of the proposed workflow, it is possible to incorporate various parameters including injection flow rate, temperature, and location. For the field of study, optimum injection well location is found to be in the southeastern part of the field. Specific locations resulting from the workflow indicated a consistent search space, having higher values in that particular region. When studied with fixed flow rates (2500 and 4911 m(3)/day), a search run through the whole field located two locations which are in the very same region resulting in consistent predictions. Further study carried out by incorporating effect of different flow rates indicates that the algorithm can be run in a particular region of interest and different flow rates may yield different locations. This analysis resulted with a new location in the same region and an optimum injection rate of 4000 m(3)/day). It is observed that use of neural network, as a proxy to numerical simulator is viable for narrowing down or locating the area of interest for optimum well placement.


Optimization of well placement in complex carbonate reservoirs using artifical intelligence
Uraz, İrtek; Akın, Serhat; Department of Petroleum and Natural Gas Engineering (2004)
This thesis proposes a framework for determining the optimum location of an injection well by using an inference method, Artificial Neural Networks and a search algorithm to create a search space and locate the global maxima. Theoretical foundation of the proposed framework is followed by description of the field for case study. A complex carbonate reservoir, having a recorded geothermal production history is used to evaluate the proposed framework ( Kizildere Geothermal field, Turkey). In the proposed fram...
Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03)
In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites
Kockan, Umit; Ozturk, Fahrettin; Evis, Zafer (2014-01-01)
In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted ...
A neuro-fuzzy MAR algorithm for temporal rule-based systems
Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04)
This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...
Neural identification of dynamic systems on FPGA with improved PSO learning
Cavuslu, Mehmet Ali; KARAKUZU, CİHAN; KARAKAYA, FUAT (2012-09-01)
This work introduces hardware implementation of artificial neural networks (ANNs) with learning ability on field programmable gate array (FPGA) for dynamic system identification. The learning phase is accomplished by using the improved particle swarm optimization (PSO). The improved PSO is obtained by modifying the velocity update function. Adding an extra term to the velocity update function reduced the possibility of stucking in a local minimum. The results indicates that ANN, trained using improved PSO a...
Citation Formats
S. Akın and M. V. Kök, “Optimization of well placement geothermal reservoirs using artificial intelligence,” COMPUTERS & GEOSCIENCES, pp. 776–785, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32866.