Static and Dynamic Uncertainty Management for Probabilistic Volumetric and Production Forecast: A Case Study from Onshore Abu Dhabi

Abstract

The concept of uncertainty, risk, and probabilistic assessment is increasingly employed as a standard in the E&P industry to assist in optimum development and investment decisions. The studied Onshore Abu Dhabi field is a Cretaceous complex carbonate oil producing reservoir, which has more than 15 years of production history. This paper discusses an integrated static and dynamic workflow to create a range of probabilistic simulation models to forecast oil production under several production schemes. The study deals with quantitative identification and ranking of factors affecting volumetric and reserves uncertainty in the field. In order to quantify the uncertainties, the main uncertainty parameters and their respective ranges were first identified, selected, and analyzed using Experimental Design to generate a tornado plot which enables the selection of the most influential parameters on the objective function. Secondly was to build a Proxy Model that would help in defining the full probabilistic volumetric distribution on the stock tank oil initial in place (STOIIP) and Recovery Factor (RF). Five main static uncertainty parameters were selected to assess the STOIIP distribution namely structure, free water level, saturation height function, porosity, and formation volume factor. In addition, four dynamic uncertainty parameters were incorporated for reserves estimation specifically Sorw, Kv/Kh, relative permeability, and subdense layer communication. A cumulative distribution function was created in order to extract the probability cases of P10, P50 and P90 of the STOIIP. The simulation models were then built using the P50 volumetric case derived from the static model that was run with hundreds of realizations. Combinations of dynamic uncertainty parameters were simulated using Monte Carlo to define the Low, Base, and High Cases. This was done by comparison with material balance computations and streamline simulation. A stochastic combination of the STOIIP distribution and the RF sensitivities was done through an Experimental Design, Proxy Model, and Monte Carlo approaches. The Base Case model history-match was checked against the choice of parameters defining the Low and High sensitivity cases. The match data available included: oil rates, water cuts, GOR, WHP, flowing and static Pressure, and saturation profiles derived from open and/or cased hole logs. The sensitivity assessment showed that using currently available data, the two major factors affecting the volumetric uncertainty are the free water level and structure. In contrast, porosity possesses the smallest impact. In addition, Kv/Kh and relative permeability are the two main parameters affecting the RF. A number of appraisal wells will be drilled to reduce the structure uncertainty specifically in the flank areas, which will lead to further maturation of reserves. Economic calculations were performed to check that all projects pertaining to the reserves category would consider oil price, CAPEX profile, OPEX profile, well and facility life time.

Improving Water Flooding Management through Facies Remodeling and the Use of Streamlines and Finite Difference Simulators

“…More than one static model with different concepts of structural and sand distribution should be constructed. Moreover, the forecast is not considered reliable unless a good history match for pressure, oil production, water production, gas production and water injection data has been achieved [8]. In the forecast phase, multiple sensitivity cases will capture the range of forecast probabilities and the best strategy to manage the field before any expensive field implementation.…”