Water is the most commonly used injection fluid for flooding/energizing oil reservoirs. Despite oil price fluctuations, water use has continued because of its wide availability, relatively low cost, and ease of handling. Decades of research and field application experiences have yielded a sound theoretical approach and practical knowledge of the subject. Nevertheless, water injection deployment and operations can still benefit from optimization. This paper discusses the state-of-the-art use of numerical optimizers based on smart algorithms and stochastic machines that couple subsurface, surface, and economic models.

During planning and operations of waterflooding projects, many decisions are made, such as the number, location, and drilling sequence of new injector and producer wells, total and per well injection rates, well conversion, and fluid withdrawal rates. In addition, each decision variable has multiple options, which combined can generate hundreds or thousands of scenarios, raising the key question of how the optimum scenario can be determined in a timely manner. Furthermore, the optimum scenario selection process should consider uncertainty (e.g., reservoir properties and oil prices) as well as operational constrains.

Based on previous experience, a general workflow was developed and fine-tuned to help identify optimum scenarios. The workflow begins by defining the scenario matrix using available validated history-match models. Models are coupled with an automatic optimizer/stochastic machine. The study cases considered reservoirs with heavy-to-medium oil, injection by pattern and flank, large variations in original oil in place (OOIP), and number of wells for waterflooding implementation and reactivation planning.

Optimization runs typically require hundreds of iterations to approach the maximum or minimum objective business function. Each iteration corresponds to a scenario. To identify the optimal scenario quickly, various strategies were tested: parallel computing and new methodologies of sequential optimization with reduced number of decision variables, initial exploratory runs with a shortened economic horizon time, and stochastic analysis of selected scenarios of the optimization run. All of these strategies proved successful, depending on the specific situation.

The workflow application in three case studies yielded approximately 30% cumulative production and net present value (NPV) increments, with less economic risk than the traditional deterministic simulation approach and reduced water cut up to 40%; compared to base scenarios, Np and NPV increases higher than 200% were obtained. Furthermore, the workflow application generated a large number of scenarios that provided flexibility to modify operations during unexpected events.

Optimizers/stochastic machines were determined to be a valid means to quantitatively estimate the economy and risks and are a fundamental tool for managing waterflooding projects, resulting in better scenarios than the traditional deterministic approach. The approach is also applicable to all types of enhanced oil recovery (EOR) projects.

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