Application
of Numerical Simulation
and Artificial Neural Network for Oil-Field
Development, SI-A Field
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
field is a large offshore producing field 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 field raised some concerns and prompted some
reservoir
studies on the field to possibly diagnose the problem and provide
some remedies to stop further decline of the field. The pilot development
performed is the basis for potential future field development, and more wells
need to be drilled to ensure a good recovery in this low-permeability field.
This
study
applies a methodology for optimizing 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 field
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 field-development schemes using an artificial neural
network in conjunction with numerical
reservoir
simulation
for the SI-A field.
In this method, a few field 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 field-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