Optimization of Well Placement to Minimize the Risk of Scale Deposition in Field Development

Determining an optimal location and configuration of well to be drilled is a critical reservoir development decisions as it can cost millions of dollars and determine the volume of hydrocarbons being produced. This is a very challenging task due to large number of decision variables involved. One important factor to consider in field development is scale deposition. It can cause production problem and reduce hydrocarbons recovered. In the extreme case, it can cause a production well to be abandoned as a result of formation damage in the near wellbore or narrowing of the production tubing. Due to the potential high cost of scale management, scale risk deposition would be an important issue.

The aim of the paper is to optimize the placement of new wells in a field development accounting for the risk of sulphate scale deposition. This scale is formed when sulphate rich seawater, usually used as injection fluid to maintain reservoir pressure, mixes with barium rich formation brines. Multi-Objective Optimization (MOO) is used to find a range of solutions by maximizing oil recovery and minimizing scale risk. Scale risk deposition is modelled by tracking the injected seawater concentration, which acts as a natural tracer, in the produced water from each well. Penalty parameter is introduced in the optimization workflow and quantified by relating the scale risk impact in the reservoir production to scale treatment (bullhead-squeeze) as the mitigation. The study used a modified version of the benchmark reservoir model, PUNQ-S3; the model was adapted to be developed using seawater flooding, and as a result sulphate scale deposition is of a major concern. Particle Swarm Optimization (PSO), a stochastic population-based and gradient-free optimization algorithm, is applied to search over the new well locations and their completion zones in the presence of potential scale deposition risk.

The paper compares a conventional field optimization with objective only to maximize oil production to the one with accounting scale risk as extra information. The paper demonstrates that multi-objective optimization allows us to identify well locations and completion zones with high recovery and yet low scale risk, leading to significant economic benefit in a number of field development scenarios.