Estimating the fracture gradient coefficient using neural networks for a field in the Middle East

Abstract Fracture gradient constitutes an essential parameter in the pre-design stage of drilling operations, reservoir exploitation and stimulation. Over the years, fracture gradient has been acknowledged to be one of the most important key element that needs to be accurately determined in order to achieve optimum operation results. Several calculation methods have been presented in the literature for different regions of the world. All of these techniques were based on either parametric or empirical correlations, which require a prior knowledge of the functional form. Moreover, these methods are only valid at the area where they were developed. This paper presents an artificial neural network model to predict the fracture gradient for the Middle East region. The data set used in the study consists of more than 21,000 points with fracture gradient measurements taken from 16 wells drilled in seven geological prospects covering more than 200 sq mi. The excellent results obtained from the developed model establish a new simple mechanism for fracture gradient prediction that is based on readily obtainable parameters. The proposed neural network model predicts the fracture gradient as a function of pore pressure, depth and rock density. A detailed comparison between the results predicted by this method and those predicted by other techniques is presented.

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