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A neural approach to sand production prediction in wells
e
Pergamon
PH:
Int. J. Rock Mech. Min. Sci. Vol. 35, No. 4/5, p. 489, Paper No. 060, 1998 © 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain S0148-9062(98)00076-X ISBN: 0080433332 ISSN: 0148-9062/98 $19.00 + 0.00
A Neural Approach to Sand Production Prediction in Wells M. KANJt J.-c. ROEGIERSU Paper No. 060§ Full paper on enclosed CD-ROM Sand production prediction refers, mainly, to the process of forecasting the instability of "load-bearing" particles at the sand face of a producing formation. It involves predicting failures in the perforation cavity and assessing sanding rates over time. Should the prevailing conditions of fluid flow and drawdown pressure imply an uncontrollable/catastrophic particle production, alternatives to natural completions must be investigated and proper sand-exclusion techniques implemented (e.g. production and completion practices and mechanical, chemical, stimulation, or combined methods). Many factors are believed to affect the tendency of a well to produce sand with the hydrocarbons. Such factors include flowrate; formation cementation, natural permeability and compressibility; reservoir internal pressure; pay-zone depth; interval length; perforation characteristics; formation sand characteristics; produce type and phases; formation damage; and drawdown. Experiments have shown that solid production is mainly attributed to changes in production rate, water breakthrough, multiphase flow, and reservoir depletion. However, no clear interrelationship between all "known variables" exists to date. Although several researchers have investigated the correlation between various factors responsible for sand production and developed a number of analytical and numerical sand production prediction methods, no one technique is universally accepted for predicting its onset and/or quantifying its occurrence. Despite considerable research on the subject of sanding prediction since the early '70 s, the capabilities in this area are still limited and not well developed. Accordingly, the problem seems to lend itself most to a powerful technology known as Artificial Neural Networks (ANNs). ANNs mimic our understanding of the "animal" brain with their potentiality to provide some of the human characteristics of problem solving that are difficult to simulate using any of the logical, analytical, numerical, or standard programming techniques. Properly trained, they predict outcomes, generalise about problems, and recognize different patterns of the domain. Basically, they behave based on what they were taught not on what they were instructed. Therefore, they require no rules, no equations, and no conventional programming. This paper aims at proving the need for making available an ANN for sand production prediction and at describing the many steps involved in the design and training of such a neurocomputational network. The paper also presents a prototype neuronet that is developed based on data generated by the SITEX system. The software is a sand inflow treatment program combining numerical, analytical and heuristic approaches to sand production prediction and control. The prototype network has a feedforward structure and is based on the backpropagation learning paradigm. The input-vector consists of the following elements characterizing the particular well, the formation, and the reservoir: porosity; clay content; formation age and condition; oil API gravity; and drawdown pressure. The network output is a single-component vector denoting the total of sanding assessment points for the particular well. The output number reflects the tendency of the well to produce sand with hydrocarbons. The trained network showed a robust performance and was able to successfully associate the input data with the proper sanding assessment/likelihood in each test case. Moreover, the system displayed a remarkable speed, accuracy, and fault tolerance to noises in the data set. Key words-sanding, sand production prediction, load-bearing particles, artificial intelligence, artificial neural networks, parallel distributed processing, adaptive systems, feedforward networks, backpropagation algorithms, hybrid intelligent systems
tRock Mechanics Institute, The University of Oklahoma, Norman, Oklahoma 73019-0628, U.S.A. tSchool of Petroleum and Geological Engineering, The University of Oklahoma, Norman, Oklahoma 73019-0628, U.S.A. §Conference Reference: USA-331-2 489
Pergamon
PH:
Int. J. Rock Mech. Min. Sci. Vol. 35, No. 4/5, p. 489, Paper No. 060, 1998 © 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain S0148-9062(98)00076-X ISBN: 0080433332 ISSN: 0148-9062/98 $19.00 + 0.00
A Neural Approach to Sand Production Prediction in Wells M. KANJt J.-c. ROEGIERSU Paper No. 060§ Full paper on enclosed CD-ROM Sand production prediction refers, mainly, to the process of forecasting the instability of "load-bearing" particles at the sand face of a producing formation. It involves predicting failures in the perforation cavity and assessing sanding rates over time. Should the prevailing conditions of fluid flow and drawdown pressure imply an uncontrollable/catastrophic particle production, alternatives to natural completions must be investigated and proper sand-exclusion techniques implemented (e.g. production and completion practices and mechanical, chemical, stimulation, or combined methods). Many factors are believed to affect the tendency of a well to produce sand with the hydrocarbons. Such factors include flowrate; formation cementation, natural permeability and compressibility; reservoir internal pressure; pay-zone depth; interval length; perforation characteristics; formation sand characteristics; produce type and phases; formation damage; and drawdown. Experiments have shown that solid production is mainly attributed to changes in production rate, water breakthrough, multiphase flow, and reservoir depletion. However, no clear interrelationship between all "known variables" exists to date. Although several researchers have investigated the correlation between various factors responsible for sand production and developed a number of analytical and numerical sand production prediction methods, no one technique is universally accepted for predicting its onset and/or quantifying its occurrence. Despite considerable research on the subject of sanding prediction since the early '70 s, the capabilities in this area are still limited and not well developed. Accordingly, the problem seems to lend itself most to a powerful technology known as Artificial Neural Networks (ANNs). ANNs mimic our understanding of the "animal" brain with their potentiality to provide some of the human characteristics of problem solving that are difficult to simulate using any of the logical, analytical, numerical, or standard programming techniques. Properly trained, they predict outcomes, generalise about problems, and recognize different patterns of the domain. Basically, they behave based on what they were taught not on what they were instructed. Therefore, they require no rules, no equations, and no conventional programming. This paper aims at proving the need for making available an ANN for sand production prediction and at describing the many steps involved in the design and training of such a neurocomputational network. The paper also presents a prototype neuronet that is developed based on data generated by the SITEX system. The software is a sand inflow treatment program combining numerical, analytical and heuristic approaches to sand production prediction and control. The prototype network has a feedforward structure and is based on the backpropagation learning paradigm. The input-vector consists of the following elements characterizing the particular well, the formation, and the reservoir: porosity; clay content; formation age and condition; oil API gravity; and drawdown pressure. The network output is a single-component vector denoting the total of sanding assessment points for the particular well. The output number reflects the tendency of the well to produce sand with hydrocarbons. The trained network showed a robust performance and was able to successfully associate the input data with the proper sanding assessment/likelihood in each test case. Moreover, the system displayed a remarkable speed, accuracy, and fault tolerance to noises in the data set. Key words-sanding, sand production prediction, load-bearing particles, artificial intelligence, artificial neural networks, parallel distributed processing, adaptive systems, feedforward networks, backpropagation algorithms, hybrid intelligent systems
tRock Mechanics Institute, The University of Oklahoma, Norman, Oklahoma 73019-0628, U.S.A. tSchool of Petroleum and Geological Engineering, The University of Oklahoma, Norman, Oklahoma 73019-0628, U.S.A. §Conference Reference: USA-331-2 489