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Biostimulation is a method of microbial enhanced oil recovery (MEOR) by injecting nutrients into an oil well to stimulate the growth of indigenous bacteria and their activities dealing with the increasing of oil recovery. This study aimed to obtain an optimum nutrient formulation at laboratory scale to be applied for MEOR field biostimulation pilot project. Field monitoring of chemical and bacterial composition in oil well was also carried out up to twelve weeks after biostimulation. The oil composition was analyzed by GC-MS and column chromatography. Bacterial community analysis was performed using 16S rRNA gene as the biomarker, which was sequenced by Illumina Miseq Sequencing. Based on Response Surface Methodology (RSM), the optimum nutrient concentrations that increase bacterial growth and decrease oil viscosity were molasses 5% (w/v), NPK 0.5% (w/v), and DAP 0.5% (w/v). The relative abundance of the bacteria and the community composition constantly changed during the monitoring process. Injection of molasses stimulated the growth of the hydrocarbonoclastic bacteria until the end of monitoring period.

Well placement optimization techniques that use reservoir simulations are currently taking advantage of using the Genetic Algorithm method and involve the output of a reservoir simulation, which is the hydrocarbon recovery, and thus, the technique requires running a reservoir simulation when finding a maximum value for the recovery. For a very large field of gas, a condensate reservoir would be very time consuming, and when there is only a limited amount of time for decision making, this approach would not be a sufficient technique. Of course, the conventional, traditional trial-and-error technique requires more effort. To address this very common challenge in field development planning, we propose the concept of transferring the manual traditional technique into a novel tool technique that employs Genetic Algorithm (GA), which can be used as a plug-in software application. This paper employs a specifically formulated Genetic Algorithm method for applications in well location optimization by introducing a newly proposed fitness function (objective function) that was constructed from basic reservoir engineering properties, i.e., permeability, porosity, oil saturation, pressure of reservoir, and thickness. Furthermore, this Genetic Algorithm method was then further extended to consider the drainage radius, existing wells, existence of faults and multiple layers, simultaneously. Hence, a software application has been developed that incorporates all of these concerns into a rapid tool. Reservoir modeling cases of oil and gas fields were used to test the proposed method, with the intention of showing the rapidness of finding the well locations, and as an additional output, this approach could yield a higher recovery than the previous technique, overall. The oil field is for cases in which multiple wells penetrate multi-reservoirs typically and penetrate selected reservoirs in test cases. However, the gas field application is for the case of horizontal well placements in which the direction and length are the optimized parameters, which are optimized by employing an Artificial Neural Network (ANN) method for the length optimization after having the best direction obtained from the Genetic Algorithm (GA) method. The proposed method can give hydrocarbon recovery results in a much faster way and even better values than the conventional method.