Structural Uncertainty Analysis using 3D Seismic and Well Data to Estimate Gross Rock Volume GRV Ranges in Reservoir: A Case Study in Carbonate Reservoir, UAE

摘要

An uncertainty analysis involves the study of different scenarios with multiple realizations of those scenarios. Structural uncertainty analysis deals with the estimation of uncertainty only due to structural components. In any model, there are a tremendous amount of uncertain parameters. It is important to establish key parameters to be investigated for the better understanding of the model. Most important parameters influencing structural uncertainty using 3D seismic data are Horizon Picking, Velocity Modeling, Well Correlation / Marker Picking & Seismic Data Processing. The motivation for the present study was the limited understanding of structure, especially, in the flanks of the field. Prognoses made in this area continue to both overestimate and underestimate formation depth and thickness. Given the average depth of the reservoir formation, several meters can have a significant impact on saturation and therefore productivity and reserves estimates. Additionally, this area has been marked as a viable location for future development projects. Capturing structural uncertainty ranges helped to understand the risks involved during well placement and provided development team, enough time, to investigate alternative locations. Structural uncertainty study using 3D seismic data was attempted to capture the GRV ranges in the reservoir with the ultimate aim to build factual static & dynamic models for optimum field development. Time-Depth (TD) conversion often accounts for more than 50% of the GRV uncertainty. This case study presents systematic methodology applied for capturing structural uncertainties appearing due to variability in horizon picking & velocity modeling. These were two key parameters used for TD conversion. The first input to the study was the sets of alternative horizon interpretations predominantly done on seismic data. Inputs from various seismic attributes were used to guide the interpretations in the areas of poor seismic data quality. The other input was the sets of alternative velocity models for the field. These models were generated using the combination of well velocities (VSP/Checkshots/Sonic) and Pre-Stack Depth Migration velocity cube. A layer cake modelling approach was used for velocity modelling. Multiple depth surfaces have been generated using different sets of horizon interpretations and velocity models. Generated surfaces were analyzed with well markers at well locations. A statistical approach has been taken to estimate structural uncertainty as 1st standard deviation (SD) depth errors in the reservoir. Multiple realizations on the 3D structural model were completed using Sequential Gaussian Simulation (SGS) for uncertainty calculations. SGS was used to generate an error surface. This error surface was applied on Base Case structural model to perform ‘n’ stochastic realizations in order to generate ‘n’ depth scenarios. Multiple depth scenarios were later used to calculate GRV ranges in the reservoir.