Offshore Angola oil field: Multiple Point Statistics (MPS) modelling approach

Aim of these work is to implement the Multiple Point Statics “MPS” technology (using the Schlumberger Petrel 2017.1 software) to an Angolan offshore oil field already in production, comparing the results with the one coming from the existing standard modeling technique. This is achieved extending the stochastic simulation approach to the sedimentological model by means of the MPS algorithm. Considering that the MPS is a modern modelling technique on the geostatistical panorama, the core concept is related with the aim of better representing the subsurface geology of the reservoir, by means of a deep analysis of the sedimentological model and the Environments of Deposition “EoD”. This can be achieved by the wide integration of data from the training image and secondary data (probabilistic data). The Environments of Deposition (EoDs) coming from the sedimentological interpretation are used to build the Training Images (conceptual models) for each zone and the depositional model, instead of being the product of a deterministic interpretation, is the result of a MPS simulation. Moreover, the existing EoD property is converted into a probabilistic trend and used to condition the simulation. The substitution of the vertical boundaries among the EoDs with more realistic depositional interfaces slightly reduces the global sand content – and the STOIIP accordingly – but it improves the geological imaging of the field and the hydraulic communication among the depositional bodies. The lithological facies and the petrophysical properties are distributed following the same approach of the current model (standard modelling approach) in order to properly evaluate the impact of the MPS algorithm on the STOIIP. Therefore, the reduction of the static hydrocarbon volume in place does not necessarily lead to a reduction of the ultimate recovery, due to a possible more efficient horizontal and vertical dynamic efficiency. The dynamic test (in both terms of HM and forecast) represents the most relevant way forward of the work, considering that the field is already in production since almost 2 years.