Neural-Network Approach To Predict Well Performance Using Available Field Data

Abstract

Abstract Accurate prediction of future well performance is of great importance for petroleum reservoir management. This paper presents a practical neural network approach to predict existing and infill oil well performance using available filed data, such as well production history and well configuration information. It serves as a practical, cost-effective and robust tool for oilfield production and management. Well production, well spacing and the time-dependent information are used to train the neural network. The time-dependent information of wells are incorporated in a manner of time series for establishment of neural network. After the neural network is established, it is used to predict future performance of existing and infill wells. No reservoir data is currently used in the establishment of neural network, therefore it can predict well production performance in absence of reservoir data. Primary production of two data sets (each has 9 wells) in North Robertson Unit located in west Texas was tested using this approach. The results demonstrate that this approach is powerful in rapid projection of existing wells’ future performance, as well as the performance prediction of infill drilling wells. Introduction Neural network is a kind of artificial intelligence technology. It mimics characteristics of biological neurons. Like human information processing system, artificial neural system, or neural network, acquire, store, and utilize knowledge by learning. The knowledge is embedded in the networks that can be recalled in response to the presented information. There are several types of neural networks. The back-propagation neural network is one of them and has been most commonly used for engineering purposes. The structure of back-propagation neural network is sketched in Fig. 1. The neural network usually consists of at least three layers. They are input layer, hide layer and output layer. Each layer has a number of neurons or nodes. Each input neuron contains actual data introduced to the network externally. The hidden layer between input layer and output layer can be one or more than one according to different applications. Each neuron in output layer give a response for a given data set. The neurons between layers are interconnected by numbers called weights.These weights determine how a particular set of inputs be sent to neurons in hidden layer, and so on from hidden layer to neurons in output layer. These weights are determined by a process called training. Establishment of a neural network is to train the neural network by adjusting the weights between neurons so that the network can give desired output sets for given input data sets by using the trained weights. In the process of training, the weights are first randomized between the range of 0 to 1. The input data matrix is then multiplied by the weights connecting input layer and hidden layer to produce a new matrix. A transfer function is used to transfer this matrix to another matrix, which is the output of middle layer neurons. The obtained matrix is multiplied by the weights associated with the middle - output layer to generate a new matrix. Similarly, a transfer function is used to transfer the matrix from middle layer to output from the output layer neurons. The obtained output results are compared with the desired outputs to calculated the discrepancy. The weights are updated based on the discrepancy from output layer to input layer (back-propagation) until a specified convergence criterion is satisfied for all input data sets and desired output sets. In recent years, there are increasing applications in the oil and gas industry1–7. Neural network has been used in various petroleum engineering areas, such as geology, geophysics, drilling and completion, formation evaluation, production and stimulation, reservoir engineering and economic etc. Neural network ha s also been proved as a very successful approach on time series prediction problems. Good examples include prices of stock market, etc.8–10