Optimizing Development Strategy and Maximizing Field Economic Recovery through Simulation Opportunity Index

Abstract

With the rapidly increasing costs of field development, one of the many challenges for any green or brown field development is to optimize the number and locations of development wells that would yield maximum sweep efficiency and recovery. A full field simulation model is generally constructed to assist in optimizing the development plans and in many occasions initial well planning is based on fixed well length, well orientation, well spacing, etc., before any iterative sensitivity runs are performed. It is recognized that in most cases, the decision was heavily influenced by a single reservoir property, such as permeability, without considering the effect of other inter-related parameters, such as movable oil volume, fluid viscosity, hydrocarbon pore volume, reservoir permeability-thickness and residual oil saturation. The need to analyze these variables in a structured and logical manner is therefore a challenge to ensure a technically sound decision making process. In this paper a modified approach, based on techniques already established (Molina et al., 2009), hereinafter called "Simulation Opportunity Index" (SOI) is introduced. It provides an innovative view on how a development strategy is addressed, and subsequently forms the basis of well placement optimization. All steps and equations to generate the desired indexes are discussed in this paper. We have successfully utilized industrial geological modeling software to perform the calculations and eventually generate the three main index properties in a 3D reservoir model. Using an in-house well planning tool, we can place new wells to optimize field development. This paper also presents a case study for a major oil field development in Saudi Arabia. Comparison between conventional approach and this modified technique clearly showed an improved field recovery performance.

Opening New Opportunities With Fast Reservoir-Performance Evaluation Under Uncertainty: Brugge Field Case Study

“…In this work, this measure is the opportunity index (OI) introduced in Molina and Rincon (2009); other measures of similar nature, such as the simulation-opportunity index presented by Abd Karim and Abd Raub (2011), could also be considered. To capture reservoir heterogeneity in the distribution of well-drilling locations, we first divide the reservoir into clusters.…”

“…In particular, we studied three renowned derivative-free algorithms: bound optimization by quadratic approximation (Powell 2009), particle-swarm optimization (Kennedy and Eberhart 1995), and the surrogate-management framework (Booker et al 1999). The cost function was again the average NPV over the same eight realizations selected previously.…”

Infill Well Location Optimization Method based on Remaining Oil Recoverable Potential Evaluation

“…Molina et al proposed a more commonly used formula for calculating the development potential index [32]. Karim et al further combined the static and dynamic characteristics and carried out the corresponding development potential evaluation [10,[33][34][35]. Geng et al considering the nonlinear relationship between remaining oil diversion ability and water saturation, introduced dominant potential abundance to quantitatively characterize the distribution of remaining oil potential in high water cut reservoirs [36].…”