ANN for oil industry

What is it about?

Purpose – Artificial neural network (ANN) is considered a good solution for building non-linear relationship between input and output parameters, which is suitable for solving production back allocation, which is the most important step for production planning of petroleum mine. The purpose of this paper is to suggest a solution for solving production back allocation problem at Samarang petrol mine based on ANN approach. Design/methodology/approach – In this study, well operational parameters’ surveillance was conducted and ANN was used to build relationships between operation parameters and production rates. Experimental method is used for testing and evaluating the possibility of using ANN for supporting production planning at Samarang mine. Findings – Consequently, the proposed ANN solution can increase the accuracy of predicted values and could be used for supporting production planning at Samarang mine. Because ANN uses well test data for training and predicting (without adding new devices), it could be a feasible and cheap solution. Research limitations/implications – There is a need for applying other methods, such as: support machine vector, non-linear autoregressive models, etc. for better evaluation of ANN solution.

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Why is it important?

Practical implications – The ANN models helped operation engineers to understand well production performance and make decision to improve production plan in timely manner. This solution could be generalized for the whole mine or to similar petroleum mines in practice. Originality/value – This paper aims to propose a solution based on ANN for solving production back allocation problem of petroleum industry. The solution is tested at Samarang mine.