Neural Network: What It Can Do for Petroleum Engineers
Authors: Mohaghegh Shahab 
Published: December 2007

Neural network, a nonalgorithmic, nondigital, intensely parallel and distributive information processing system, is being used more and more everyday. The main interest in neural networks is rooted in the recognition that the human brain processes information in a different manner than conventional digital computers. Computers are extremely fast and precise at executing sequences of instructions that have been formulated for them. A human information processing system is composed of neurons switching at speeds about a million times slower than computer gates. Yet, humans are more efficient than computers at such computationally complex tasks as speech and other pattern-recognition problems.
Artificial neural systems, or neural networks, are physical cellular systems that can acquire, store, and use experiential knowledge. The knowledge is in the form of stable states or mapping embedded in networks that can be recalled in response to the presentation of cues. In a typical neural data processing procedure, the database is divided into two separate portions called training and test sets. The training set is used to develop the desired network. In this process (depending on the paradigm that is being used), the desired output in the training set is used to help the network learn by adjusting the weights between its neurons or processing elements.
Once the network has learned the information in the training set and has"converged," the test set is applied to the network for verification. It is important to note that although the user has the desired output of the test set, it has not been seen by the network. This ensures the integrity and robustness of the trained network.
A handful of articles on the use of neural networks in the petroleum industry has appeared in SPE conferences, proceedings, and publications in the past 2 years. These articles can be divided into two categories: those that use neural networks to analyze formation lithology from well logs and those that use neural networks to pick a reservoir model to be used in conventional welltest interpretation studies. These tasks are usually done by log analysts and reservoir engineers, and their automation using a fault-tolerant process may prove valuable.
Neural networks can help engineers and researchers by addressing some fundamental petroleum engineering problems as well as specific ones that conventional computing has been unable to solve. Petroleum engineers may benefit from neural networks on occasions when engineering data for design and interpretations are less than adequate. This is an especially common occurrence in the Appalachian basin, where some fields are quite old. Lack of adequate engineering data may also be encountered because of the high cost of coring, well testing, and so on.
Neural networks have shown great potential for generating accurate analysis and results from large amounts of historical data that otherwise would seem not to be useful or relevant in the analysis. An example of such a problem was encountered by a gas company for a gas storage field in Ohio. In the absence of appropriate data, which normally would make engineering design and evaluation of the fracturing jobs virtually impossible, a carefully designed neural network was able to predict the performance of fracturing jobs with great accuracy. A linear plot of the actual fracturing job results (data never seen by the network during training) and network predictions resulted in a correlation coefficient of 0.98, where 1.00 is a perfect match.
Neural networks have proved to be valuable pattern-recognition tools. They are capable of finding highly complex patterns within large amounts of data. A relevant example is well log interpretation. It is generally accepted that there is more information embedded in well logs than meets the eye. Determination, prediction, or estimation of formation permeability without actual laboratory measurement of the cores or interruption in production for well test data collection has been a fundamental problem for petroleum engineers. From geophysical well log data, it was possible to predict and/or estimate permeability of a highly heterogeneous formation in West Virginia, as shown in Fig 1.

Reference
Mohaghegh, S., Arefi, R., and Ameri, S.: "Design and Development of an Artificial Neural Network for Prediction of Formation Permeability,"paper SPE 28237 presented at the 1994 SPE Petroleum Computer Conference, July31-Aug. 3, Dallas.