--> Abstract: Application of Numerical Simulation and Artificial Neural Network for Oil-Field Development, SI-A Field, by Fariba Salehi, Ronak Azizi, Arnoosh Salehi, Amir Taheri, and Vali A. Sajjadian; #90067 (2007)
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Application of Numerical Simulation and Artificial Neural Network for Oil-Previous HitFieldNext Hit Development, SI-A Previous HitFieldNext Hit

 

Fariba Salehi1, Ronak Azizi1, Arnoosh Salehi2, Amir Taheri3, and Vali A. Sajjadian4, (1) Karaj University, (2) Pars Oil & Gas Company, (3) Research & Development of NIOC, (4) Arvandan Oil & Gas Company  [email protected], [email protected]

 

SI-A Previous HitfieldNext Hit is a large offshore producing Previous HitfieldNext Hit located 100 km off the Iranian shore, close to the IranÐEmirate border. SI-A reservoir is situated in the Ilam formation, which is divided into several layers. The Ilam formation has been deposited in shallow marine conditions. This is a north-south elongated anticline of 46,000 feet x 23,000 feet with maximum vertical closure of 490 feet. The original oil in place is estimated to be around 2 MMMSTB. The sharp decline encountered in the Previous HitfieldNext Hit raised some concerns and prompted some reservoir studies on the Previous HitfieldNext Hit to possibly diagnose the problem and provide some remedies to stop further decline of the Previous HitfieldNext Hit. The pilot development performed is the basis for potential future Previous HitfieldNext Hit development, and more wells need to be drilled to ensure a good recovery in this low-permeability Previous HitfieldNext Hit. This study applies a methodology for Previous HitoptimizingNext Hit well placement by numerical simulation and artificial neural network. Optimum location of an oil and gas well depends on many factors. Numerical simulation is the conventional and convenient way to evaluate these factors. Optimization techniques require an abundant number of function evaluations to find the optimum; thus, generally it is not possible to carry out a sufficient number of simulations. In Previous HitfieldNext Hit development studies, a large number of scenarios, which result in a time-consuming and expensive process must be considered. The objective of this paper is to structure the Previous HitfieldNext Hit-development schemes using an artificial neural network in conjunction with numerical reservoir simulation for the SI-A Previous HitfieldNext Hit. In this method, a few Previous HitfieldNext Hit development scenarios are studied using a numerical simulator. The results of these studies are used to train the ANN. The trained ANN is then used as a predictive tool for Previous HitfieldTop-development purposes. Using NS-ANN, the number of numerical simulations is significantly reduced. The NS-ANN approach provides the flexibility of considering any location as a potential site in contrast to the conventional simulation approach when the well locations are restricted to the pre-defined block centers. The NS-ANN approach is faster and more efficient than its conventional counterpart. The results obtained from NS-ANN compare well with the results obtained from a reservoir simulator.

 

 

AAPG Search and Discover Article #90067©2007 AAPG Mid-Continent Section Meeting, Wichita, Kansas