ABSTRACT

A methodology to generate synthetic wireline logs is presented. Synthetic logs can help analyze the reservoir properties in areas where the set of logs that are necessary, are absent or incomplete. The approach presented involves the use of Artificial Neural Networks as the main tool, in conjunction with data obtained from conventional wireline logs. Implementation of this approach aims to reduce costs to companies.

Development of the neural network model was completed using Generalized Regression Neural Network, and wireline logs from four wells that included gamma ray, density, neutron, and resistivity logs. Synthetic logs were generated through two different exercises. Exercise one involved all four wells for training, calibration and verification process. The second exercise used three wells for training and calibration and the fourth well was used for verification. In order to demonstrate the robustness of the methodology, three different combinations of inputs/outputs were chosen to train the network. In combination "A" the resistivity log was the output and density, gamma ray, and neutron logs, and the coordinates and depths (XYZ) the inputs. In combination "B" the density log was output and the resistivity, the gamma ray, and the neutron logs, and XYZ were the inputs, and in combination "C" the neutron log was the output and the resistivity, the gamma ray, and the density logs, and XYZ were the inputs. After development of the neural network model, synthetic logs with a reasonable degree of accuracy were generated. Results indicate that the best performance was obtained for combination "A" of inputs and outputs, then for combination "C", and finally for combination "B". In addition, it was determined that accuracy of synthetic logs is favored by interpolation of data. As an important conclusion, it was demonstrated that quality of the data plays a very important role in developing a neural network model.

INTRODUCTION

Well logging has been in use for almost one century as an essential tool for determination of potential production in hydrocarbon reservoirs. Log analysts interpret the data from the log in order to determine the petrophysical parameters of the well. However, for economical reasons, companies do not always posses all the logs that are required to determine reservoir characteristics. This paper presents amethodology that can help to solve the aforementioned problem by generating synthetic wireline logs for those locations where the set of logs that are necessary to analyze the reservoir properties, are absent or are not complete. The intention of the technique used here is not to eliminate well logging in a field but it meant to become a tool for reducing costs for companies whenever logging proves to be insufficient and/or difficult to obtain. This technique in addition, can provide a guide for quality control during the logging process, by prediction of the response of the log before the log is acquired. The approach presented involves the use of artificial neural networks, as the main tool, in conjunction with data obtained from conventional wireline logs.

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