Well test model identification by artificial neural networks

The aim of this research is to investigate the performance of artificial neural networks computing technology, to identify preliminary well test interpretation model based on derivative plot. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm for a predefined range of analytically generated well test response. The trained network is then requested to identify the well test identification model for test data, which is not used during training sessions. For creation of training examples, an analytical response generator is implemented which is capable of producing responses of various models. Both the neural. network simulator and the analytical response generator is enfolded into a single package which is capable of producing diagnosis plots, transferring data and filtering the input pattern. Unlike the ones presented in literature the package utilises a distributed modular structure, by which saturation possibility of the neural network is reduced considerably. Moreover, the distributed structure allows the training sequence to be initiated on different computers, thus reducing the training time up to sixteen folds. The package is verified with several examples either analytically generated or taken from literature.
Petroleum Science and Technology


Integrated nonlinear regression analysis of tracer and well test data
Akın, Serhat (Elsevier BV, 2003-08-01)
One frequent observation from conventional pressure transient test analysis is that field data match mathematical models derived for homogeneous systems. This observation suggests that pressure data as presently interpreted may not contain details concerning certain reservoir heterogeneities. On the other hand, tracer tests may be more sensitive to heterogeneous elements present in the reservoir because of the convective nature of the flow test. In this study, a possible improvement of conventional pressure...
Determination of three-phase relative permeabilty values by using an artificial neural network model
Karaman, T; Demiral, BMR (Informa UK Limited, 2004-08-01)
In this study, an artificial neural network (ANN) tool, which uses the data obtained from a pore network (PN) model, was developed in order to obtain three-phase relative permeability values. During the development of this ANN tool, four different stages were implemented in which ANN structures were changed in order to find the best architecture that would predict the oil isoperms correctly. By using the data obtained from the PN model, training was implemented and the prediction power of that tool was test...
Modelling of two-phase flow through concentric annuli
Ozbayoglu, M. E.; Omurlu, C. (Informa UK Limited, 2007-01-01)
A mathematical model is introduced in order to predict the flow characteristics of multiphase flow through an annulus. Flow patterns and frictional pressure losses estimated using the proposed model are compared with the experimental data of a wide range of liquid and gas flow rates recorded at a flow loop consisting of numerous circular pipes and annulus. The results showed that the model predictions for flow patterns and frictional pressure losses are reasonably accurate. Moreover, it is observed that geo...
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...
Artificial neural network modeling for forecasting gas consumption
Gorucu, FB; Geris, PU; Gumrah, F (Informa UK Limited, 2004-02-01)
This study includes an approach to evaluate and forecast gas consumption by Artificial Neural Network (ANN) modeling for the capital city of Ankara, Turkey. ANN models have been trained to perform complex functions in various fields of application including the forecasting process. The process of the study is examining the factors affecting the output and training the ANNs to decide the optimum parameters to be used in forecasting the gas consumption for the remaining days of 2002 and the year 2005. During ...
Citation Formats
M. V. Kök, “Well test model identification by artificial neural networks,” Petroleum Science and Technology, pp. 783–794, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47851.