Using ANN for Well Type Identifying and Increasing Production from Sadi
Formation of Halfaya Oil Field South of Iraq -- Case Study.
Abstract
The current study focuses on utilizing artificial intelligence (AI)
techniques to identify the optimal locations for achieving the
production company’s primary objective, which is to increase oil
production from the sadi carbonate reservoir of the Halfaya oil field in
southeast Iraq, with the determination of the optimal scenario of
various designs for production wells, which include vertical,
horizontal, multi-horizontal, and fishbone lateral wells, for all
reservoir production layers. The ANN tool was used to identify the
optimal locations for obtaining the highest production from the
reservoir layers and the optimal well type. For layer SB1 the average
daily production is 291.544 STB/D with horizontal well, 441.82 STB/D for
multilateral, and 1298.461STB/D for the fishbone well type. Also, for
SB2 layer 197.966 STB/D, 336.9834 STB/D, and 924.554 STB/D, and for SB3
333.641 STB/D, 546.6364 STB/D and 1187.159 STB/D for the same well types
sequence. While the cumulative production for each formation layer is
22.440 MMSTB from the horizontal well, 59.05 MMSTB from multilateral and
84.895 MMSTB from fishbone well types for SB1 layer, Also 48.06 MMSTB,
70.1094 MMSTB, and 160.254 MMSTB for SB2, and 75.2764 MMSTB, 111.7325
MMSTB and 213.1291 MMSTB for SB3 for the same well types.