A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited grids and used in the training and testing of the used network. A comparison between the calculated and observed cumulative oil production has been carried out through the testing steps of the constructed ANN, an absolute average percentage error of the used network was reached to 4.044%, and this is consider to be an acceptable limit within engineering applications, in addition to that, a good behavior was reached with (FFNNW) and suitable re-entry wells location were identified according to the reservoir configuration (pressure and saturation distribution) output from SRF simulation model at the end of 2005.

Water flooding is one of the most important methods used in enhanced production; it was a pioneer method in use, but the development of technology within the oil industry, takes this subject toward another form in the oil production and application in oil fields with all types of oils and oil reservoirs. Now days most of the injection wells directed from the vertical to re-entry of full horizontal wells in order to get full of horizontal wells advantages.This paper describes the potential benefits for using of re-entry horizontal injection wells as well as combination of re –entry horizontal injection and production wells. Al Qurainat productive sector was selected for study, which is one of the four main productive sectors of South Rumaila oil field. A simulation model – named as SRFQ was used in the present work to predict the re-entry horizontal wells performance.Four scenarios were suggested to cover the full scope of the study; those scenarios are different in manner of wells combinations. Cumulative oil production, ultimate recovery percentage are two criteria were used to predict the performance and comparison of scenarios.Results from simulation model (SRFQ) runs revealed that the productive sector can be continue to gain 1564.331 MMSTB till 2020, without changing to any existing injection and production wells status, which is called the base scenario. While scenario no.1 needs some of work over and remedies jobs, which gives more cumulative oil production reaches to 1698.481 MMSTB till 2020.On another side, scenarios no. 2 and 4 are the most important scenarios because re-entry horizontal injection wells were implemented. Very good and encourage results were gained over the bas scenario from the sector under study.At last, scenario no.3 was suggested just to predict the production capacity of the Al Qurainat sector with re-entry horizontal production wells and existing vertical injection and production wells, while the cumulative oil production reaches 3398.481MMSTB.

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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.

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