Spatial Modeling of Oil Exploration Areas Using Neural Networks and ANFIS in GIS

TL;DR

This study uses GIS-based maps combined with neural networks and ANFIS to identify high-potential oil and gas exploration areas, aiming to reduce exploration costs and improve accuracy.

Contribution

It introduces a novel integration of GIS maps with neural networks and ANFIS for oil exploration site prediction, demonstrating improved accuracy over traditional methods.

Findings

Neural network model achieved R=0.8948 and kappa=0.9079, outperforming ANFIS.

The neural network predicted potential zones more accurately but had some false positives.

Model validation showed the neural network's effectiveness in identifying high-potential areas.

Abstract

Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise locations lead to waste of time and money during the operation. The objective of this study is to locate high-potential oil and gas field in 1: 250,000 sheet of Ahwaz including 20 oil fields to reduce both time and costs in exploration and production processes. In this regard, 17 maps were developed using GIS functions for factors including: minimum and maximum of total organic carbon (TOC), yield potential for hydrocarbons production (PP), Tmax peak, production index (PI), oxygen index (OI), hydrogen index (HI) as well as presence or proximity to high residual Bouguer gravity anomalies, proximity to anticline axis and faults, topography and curvature maps obtained from Asmari Formation subsurface contours. To model and to integrate maps, this study employed artificial neural network and adaptive neuro-fuzzy inference system (ANFIS) methods. The results obtained from model validation demonstrated that the 17x10x5 neural network with R=0.8948, RMS=0.0267, and kappa=0.9079 can be trained better than other models such as ANFIS and predicts the potential areas more accurately. However, this method failed to predict some oil fields and wrongly predict some areas as potential zones.