No abstract

Галкин Владислав Игнатьевич-доктор геолого-минералогических наук, профессор, заведующий кафедрой геологии нефти и газа (тел.:

No abstract

The paper considers probabilistic approach application to assess zonal oil and gas potential of the Visimskaya monocline area. The earlier studies have shown that the processes of generation, migration and accumulation of hydrocarbons (HC) shall be most effectually assessed from a probabilistic standpoint. Generation processes are best described by a complex probabilistic criterion – PcompG , migration and accumulation processes – by PcompMAC [1]. These criteria were used to develop a multivariate statistical model – Pcompzon , acting as an estimate of the system emergence regarding the zonal oil and gas potential. The values were ranked from maximum to minimum to assess the effect of PcompG and PcompMAC indicators on Pcompzon at different ranges of its values. We successively built and analysed 379 models. The analysis of these models has shown that at Pcompzon>0.5 there is a congruent effect of PcompG and PcompMAC on this indicator, the effect being different at Pcompzon<0.5. A pattern of its variation over the area was built based on Pcompzon values, which allowed differentiating the area of the Visimskaya monocline into a high-potential western part and a lower-potential eastern part.

Reservoir simulation models are used to design oil field developments, estimate efficiency of geological and engineering operations and perform prediction calculations of long-term development performances. A method has been developed to adjust the permeability cube values during reservoir model history-matching subject to the corederived dependence between rock petrophysical properties. The method was implemented using an example of the Bobrikovian formation (terrigenous reservoir) deposit of a field in the Solikamskian depression. A statistical analysis of the Bobrikovian formation porosity and permeability properties was conducted following the well logging results interpretation and reservoir modelling data. We analysed differences between the initial permeability obtained after upscaling the geological model and permeability obtained after the reservoir model history-matching. The analysis revealed divergences between the statistical characteristics of the permeability values based on the well logging data interpretation and the reservoir model, as well as substantial differences between the adjusted and initial permeability cubes. It was established that the initial permeability was significantly modified by manual adjustments in the process of history-matching. Extreme permeability values were defined and corrected based on the core-derived petrophysical dependence KPR = f(KP) , subject to ranges of porosity and permeability ratios. By using the modified permeability cube, calculations were performed to reproduce the formation production history. According to the calculation results, we achieved convergence with the actual data, while deviations were in line with the accuracy requirements to the model history-matching. Thus, this method of the permeability cube adjustment following the manual history-matching will save from the gross overestimation or underestimation of permeability in reservoir model cells.