This paper describes a case study combining more frequently used tools in the petroleum industry, such as volumetric analysis with Monte Carlo Simulation and Material Balance, to improve performance predictions in the carbonate mature fields offshore. Quantifying the uncertainties in original oil in place (OOIP) estimates can support development and investment decisions for individual reservoirs. In the early life of a reservoir, the well data is largely uncertain. Probabilistic estimates are commonly generated prior to significant production from a carbonate mature field by combining volumetric analysis with the Monte Carlo method.
The Monte Carlo method was used to compute the oil in place using static reservoir properties, such as petrophysical parameters, which always involve a magnitude of uncertainty and, as such, should be treated as random variables with distinct probability distributions. To assess the profitability of the development project, it was necessary to use material balance for the field.
Material balance evaluation has been identified as a useful tool for initially establishing connected hydrocarbon volume in place and for identifying reservoir drive mechanisms. This tool is often considered more accurate than volumetric methods, since the volumetric methods are based on dynamic reservoir data such as pressure and production, and thus can be applied only after the reservoir has produced for a significant period of time.
The outcome of the Monte Carlo simulation was a range of reserve values with their associated probabilities of P10, P50, and P90. A commercial material balance software was used to carry out a combination of the analytical and graphical methods establishing the correct material balance model, thereby adding confidence in the obtained results for reserves in the field. OOIP was found to be approximately 235 to 245 MMSTB, of which ~21% is stored in the matrix system. During the execution of the project, the combination of methods can reduce the non-uniqueness of the material balance solution. Material balance can reduce the uncertainty in the range estimates, since they are based on observed performance data.
Base case prediction forecasts only recovered 19% of OOIP, or an additional 9% from current recovery. This is in agreement with the references of similar fields.
Based on 40-year prediction forecasts, an additional ~72.6 to 98 MMSTB of oil-equivalent reserves can be generated with an additional capital expenditure (CAPEX) of 450 to 600 MM$ for water or gas injection facilities and 10 to 14 wells, leading to a recovery factor of ~40.9 to 46.6 of OOIP.
Field development planning is traditionally a sequential process; decisions are often segmented and disconnected. Typically, reservoir engineers model reservoir response to the bottom hole, production engineers model the whole wellbore to the well head, and process engineers model the surface facilities from the well head to the tank (Saputelli et al. 2002). For the above reasons, project results often deviate from the project plan.
In the building of field development plans, in-situ hydrocarbon reserves represent a key uncertainty to be analyzed. Because of poor predictability in hydrocarbon reserves, surface facilities may remain sub-utilized, a reservoir's full potential may not be obtained, and field economics may not reach peak performance.
Field development decisions must be made despite uncertainties in hydrocarbon reserves. The heterogeneity of information and the complexity of current hydrocarbon assets require an iterative approach to identify the best opportunities.