Michael D. McCormack - Plano TX Donald J. MacAllister - Carrollton TX Richard F. Stoisits - Plano TX Perry W. Scherer - Harrison NY Tuan D. Ma - Anchorage AL
International Classification:
G06F 1900
US Classification:
702 13
Abstract:
A system for producing a material balance solution for well patterns in a hydrocarbon reservoir is described that automatically optimizes the fluid allocation factors for each well used in determining the solution. The system automatically optimizes estimates for the allocation factors to be used in the material balance solution by randomly generating a first generation of allocation factor strings, each string in the generation assigning allocation factors to each of the wells in the reservoir. A fitness function value is determined for each of the strings by evaluating a fitness function, wherein the fitness function comprises the sum of the differences between computed and measured field pressures for each pattern, and the sum of the differences between target allocation factors and the allocation factors specified within the string for each well. A succeeding generation of allocation factor strings is produced according to a genetic algorithm. The process of determining a fitness function value for each of the strings is then repeated for the succeeding generation.
Donald J. MacAllister - Carrollton TX Virginia W. Pennington - Dallas TX
Assignee:
Atlantic Richfield Company - Los Angeles CA
International Classification:
E21B 4316 E21B 4320
US Classification:
166269
Abstract:
A method for recovering hydrocarbons from a subterranean hydrocarbon-containing formation penetrated by at least two wellbores, said method comprising: (a) injecting a gaseous stream into the formation near the bottom of the formation through a first wellbore; (b) injecting an aqueous stream into the formation near the top of the formation through the first wellbore; and (c) recovering hydrocarbons from the formation through a second wellbore.
Automated Material Balance System For Hydrocarbon Reservoirs Using A Genetic Procedure
Michael D. McCormack - Plano TX Donald J. MacAllister - Carrollton TX Richard F. Stoisits - Plano TX Perry W. Scherer - Harrison NY Tuan D. Ma - Anchorage AK
Assignee:
Atlantic Richfield Corporation - Los Angeles CA
International Classification:
G01V 338
US Classification:
702 13
Abstract:
A system for producing a material balance solution for well patterns in a hydrocarbon reservoir is described that automatically optimizes the fluid allocation factors for each well used in determining the solution. The system automatically optimizes estimates for the allocation factors to be used in the material balance solution by randomly generating a first generation of allocation factor strings, each string in the generation assigning allocation factors to each of the wells in the reservoir. A fitness function value is determined for each of the strings by evaluating a fitness function, wherein the fitness function comprises the sum of the differences between computed and measured field pressures for each pattern, and the sum of the differences between target allocation factors and the allocation factors specified within the string for each well. A succeeding generation of allocation factor strings is produced according to a genetic algorithm. The process of determining a fitness function value for each of the strings is then repeated for the succeeding generation.
Petroleum Production Optimization Utilizing Adaptive Network And Genetic Algorithm Techniques
Richard F. Stoisits - Plano TX Kelly D. Crawford - Allen TX Donald J. MacAllister - Carrollton TX Michael D. McCormack - Plano TX
Assignee:
Atlantic Richfield Company - Chicago IL
International Classification:
G05B 1302
US Classification:
700 28
Abstract:
A computer system and method of operating the same to optimize the operating conditions of a petroleum production field, in which a plurality of wells are arranged according to drill sites, and connected to one or more central processing facilities, is disclosed. In this disclosed embodiment, gas compression capacity is a significant constraint on the operation of the complex production field, and surface line hydraulic effects of well production are to be considered in the optimization. A genetic algorithm is used to generate, and iteratively evaluate solution vectors, which are combinations of field operating parameters such as incremental gas-oil ratio cutoff and formation gas-oil ratio cutoff values. The evaluation includes the operation of an adaptive network to determine production header pressures, followed by modification of well output estimates to account for changes in the production header pressure. Convergence of the genetic algorithm identifies one of the solution vectors as containing an optimal combination of field operating parameters that may be used by production personnel to set the operating conditions of the field.