Abstract
This paper presents the development of a static model for a naturally-fractured High-Pressure-High-Temperature (HPHT) gas sand reservoir located in the Tarim basin, Western China. The study is part of a well placement optimization study. It is motivated by the big challenges of drilling a well at depths ranging from 6800m-8000m[AG1] in a HPHT environment. A detailed fine-scale model is required as input for the dynamic model.
The static model is developed through an integration [AG2]process. It consists of both matrix and fractures. The matrix modeling started by integrating 3D seismic and log data to build the structural model. A new rock type scheme was developed by reconciling log and core data, including capillary pressures. Additionally, permeabilities are estimated at each uncored location using a two-step approach, namely trend estimation by regression analysis and variability simulation by 1D Gaussian simulation. The 3D modeling was executed in the order of least dependent to most dependent variable (i.e., from facies, to rock type, then followed by porosity, permeability and saturation respectively). From the geology, the sand bodies were interpreted to be continuous throughout the field. Discontinuous mudstone layers are sandwiched in-between the sand bodies. This information, together with outcrop data, is used to guide the spatial relationships in the model. Facies, rock type, porosity and permeability are simulated using geostatistical procedures. Meanwhile, saturation is generated based on the Leverett J-Function. To quantify the uncertainty in the various data, especially in the capillary pressure data, the porosity-permeability relationship, the gas-water contact and the surface tension of the gas-water system, a probabilistic model of the Gas Initially in Place (GIP) is created through uncertainty and sensitivity analysis.
The origin of the fracture system was analyzed by developing a prototype of a conceptual model. The understanding from the prototype model is coupled with the 3D seismic, outcrops, drilling information, rock mechanics, image log, core, and dynamic data, to develop fracture characteristics and correlations. The discrete fracture system is modelled using a stochastic simulation approach, constraining it to the seismically-inverted fracture density map for each zone through well-seismic correlations and a nonlinear inversion [AG3]to build the Discrete Fracture Network (DFN). Finally, the fracture model is integrated with the matrix model by upscaling the DFN model into the grid system. Following the creation of the static model, a dual porosity model was prepared for dynamic modeling by maintaining consistency between the fine scale and upscaled models throughout the upscaling process.
The methodology described above has produced a detailed fine scale model that shows consistency between properties and geology. This is a direct consequence of the new rock type system and the order in which the simulation was conducted. The facies model shows the continuity of the sand bodies, and the discontinuity of the mudstone, as indicated by the geological interpretation. The 3D Poro-Perm relationship shows the variability which is a reflection of the variability of the core data. The probabilistic distribution of the GIP is in agreement with the results of conventional reservoir engineering analyses, namely Material Balance and Rate Transient Analysis. Furthermore, the fracture distribution confirms the information both at the wells, as well as in-between the wells as given by the seismic interpretation.
This study demonstrates that a reliable fine scale model can be developed to match the available data and interpretation by properly preparing the pre-requisite inputs and following the order of dependency in the reservoir attributes.
