Estimation of oil and gas column heights in prospects using probabilistic basin modelling methods

Abstract

Source rock and reservoir rock properties are often poorly constrained in the exploration for oil and gas. Geological analogues may be used to create source rock and reservoir rock models in areas with only sparse well data coverage. Exploration risks are often influenced by uncertainties in the rock facies models. This paper outlines a methodology for assessing the consequences of using two or more source and reservoir models on hydrocarbon phase distributions in prospects. Each source rock and reservoir rock scenario is represented by computerized maps (grids) of thickness, porosities and other properties. The uncertainties of each scenario are described using probabilistic properties. The reservoir thickness may be represented by a uniform distribution with the mean described using a map (grid) with a standard deviation of, for example, 50 m. A Monte Carlo simulation is carried out for this part of the petroleum system and the resulting probabilities of oil and gas in prospects are compiled by weighting each run to calibration wells. The results can be plotted as a map of most likely oil and gas column heights. An uncertainty map is also plotted. The results can be used to rank drilling locations with respect to large oil columns and low uncertainties. The oil and gas column probabilities can be estimated for each well location. As (more) wells are drilled within a petroleum system the best match simulations can be used to further refine predictions as well as to create an updated map of the petroleum system.

Influence of fault map resolution on pore pressure distribution and secondary hydrocarbon migration; Tune area, North Sea

“…In the secondary hydrocarbon migration modelling the ‘Semi’ modelling software was used (Sylta 1993; Sylta & Krokstad 2003). Semi employs a ray tracing modelling approach, assuming that hydrocarbons migrate along the top of a permeable carrier bed below a seal and upwards along the most steeply dipping rock surface, and governed by buoyancy.…”

Seismic Inversion Methods and some of their constraints

“…Subsequently geobodies are outlined, where the probability is for instance above 70% for the porosity to be higher than 10%. As more wells are drilled in the petroleum system, the best matched simulations are retained to further refine the predictions (Sylta and Krokstad 2003).…”

An improved numerical simulator for multiphase flow in porous media

“…In the discrete equation (19), the molar mass derivative may be expressed in terms of the water pressure. This approximation is obtained from Equation (1), which using the divergence theorem reads…”

“…Substituting the molar mass derivative approximation given by Equation (27) into Equation (19) gives at each control volume…”