Well placement can be described as the positioning of production and injection

wells in an oil field to optimally deplete the reservoir. Knowledge of this key concept is a necessary requirement to effectively utilize the reservoirs natural pressure; this will ensure optimal drainage of the saturating hydrocarbons [1] . A decision on optimal well location is normally a tedious process which is affected by geologic complexities, engineering limitations and economics. These factors must be properly accounted for to avoid the adverse effect on the general performance of the reservoir and place limit to the recoverable hydrocarbon reserves. An interesting approach to solving the problem of well placement is the use of quality mapping; it is a two-dimensional representation of multiple flows through a porous medium. In the utilization of quality maps, two approaches are present: the basic quality map approach (BQM) and the modified quality map approach (MQM) [2] [3] [4] .

Another popular method of deciding well architecture is the utilization of simulation software to decide well placement positions within a reservoir. This approach utilizes information from seismic surveys, measurement while drilling, special core analysis and also logging to create an electronic sample of the reservoir. This method is applied to establish the most efficient depletion scheme which may be utilized to produce a well [5] .

In order to ensure the optimal productivity of a reservoir, some of the paramount factors which must be taken into account are the location, timing and types of well utilized for a field development project. The positioning of production and injection wells is a very critical aspect of oil field development planning (FDP), a phase which deals with the acquisition, analysis and integration of data from geologists, geophysicists and reservoir engineers for the optimal development of an oil field [6] .

Detail work has been done on optimal well placement focusing on the interaction within the surface facilities or the reservoir [7] . Henry et al. [8] used combined knowledge of geological model and reservoir properties obtained from log data to build a 3-D reservoir property model, considering facies in identifying sweet spots for optimizing well placement. The integration of the geological and reservoir model assumed negligible interaction between wells which actually reduces complexity and possible computation time, this computation may limit the wide application of the result. The method of using quality maps alongside integration programming solution was implemented by [9] ; the use heuristics algorithm technique offers a better solution efficiently for complex computational problems requiring large solution times [8] . Marques et al. [10] investigated the placement of well using reservoir drive mechanisms (gas cap and water drive) and aquifer size in three different locations: right below the oil water contact, at the middle of the oil column, and right above the oil water contact. The integration of detail engineering judgment and heuristic algorithm will possibly provide efficient result in the well placement.

The use of heuristics is fast becoming a popular methodology in the optimization of well placement [11] . With advancement in the speed of computing, more variables can be accounted for with the perspective of a larger scope for better computational result within relatively smaller time frame. Yeten et al. [12] utilized a Genetic Algorithm (GA) to maximize well location, well type and trajectory for directional wells. Asides that, they also improved and incorporated software based on nonlinear conjugate gradient algorithm to further enhance intelligent well controls.

In the field of synthetic intellectual competence, a GA is a search heuristic that mirrors or mimics the approach of normal decision. This heuristic (also occasionally called a metaheuristic) is routinely used to create accommodating responses for development and chase problems [13] . Genetic algorithms fit in with the greater class of Transformative Algorithms (TA), which make answers for headway issues using systems moved by normal advancement.

Genetic algorithms find application in bioinformatics, phylogenetic, computational science, Civil engineering/construction, budgetary angles, science, creating, math, material science, pharmacometrics and diverse fields [14] .

This study involves the use of 3-D reservoir simulator to generate data which is then run through a heuristic program. Next, the result is coupled with engineering judgment in order to generate an optimal solution to the well placement problem. Well placement is extremely a challenging task of reservoir development, nevertheless, the application of the engineering judgment to the data generated from the simulator integrated with the heuristic algorithm, helps in determining the optimum infill well location, and field development plan resulting in the substantial increase in the productivity and reserves. This principle is profitable in wells with complex fluid low and heterogeneous in nature. The heuristics for automated optimization is used for benchmarking performance of well placement. The optimal well placement into areas of the reservoir maximizes more flow area of fluid saturation, resulting in cumulative oil recovery, reduces level of uncertainty, and this in general will possibly reduce unnecessary cost that may result from inappropriate well placement.

In this study of well placement in a finch reservoir, a 3-D Reservoir simulator was used to build the model, alongside a genetic algorithm created with the use of the java compiler program for optimum placement of the well, with the engineering judgments as guiding factors in order to maximize the reservoir productivity. The varying permeability, porosity and pay zone thickness at different points in the reservoir were used in the developed algorithm to compute the injection and production points.

The injection and production data from the genetic algorithm are then inputted into the simulation in order to place the wells and assess the benefits from a multiple configuration being considered by the genetic algorithm and also to obtain the best recovery factor. The efficiency of all scenarios evaluated is then considered in terms of the recovery factor, production profiles and evolution of water cut.

Engineering judgment involves the exploitation of natural phenomena present in the reservoir. It is based on reservoir parameters such as: The presence of faults, fractures, anisotropy, connectivity, reservoir extent, flooding pattern selection, permeable zone exploitation, fluid distribution within reservoir, injection fluid selection, exploitation of reservoir geological features, gravity drainage effect, well spacing and positioning. In applying the engineering judgment, the well placement results from simulator and the GA are modified by placing injection and production wells in the following regions: unswept areas and areas which are not optimally drained by the GA derived well placement.

The model parameters used for this work is as shown in Table 1, the data extracted from the reservoir model involves the step-by-step selection of cellblocks and then reading measurements of individual cellblock parameters such as: