Abstract
Permeability is one of the important reservoir properties for oil and gas reservoirs. It directly impacts the reservoir's ability to produce and is considered a key factor to delineate reservoir sweet spots for optimum well placement and successful development strategies. However, the permeability prediction is difficult for coal seam gas (CSG) reservoirs due to the presence of complex geological conditions which cause the lateral and vertical heterogeneity on the coal seam. The objective of this study is to present a practical petrophysical approach where the estimation of coal permeability is possible under the mapping of fractures and cleats using high-resolution microresistivity image logging data. This technique provides meaningful results that can integrate better with known coal geological criteria such as ranking and lithotypes. Furthermore, it helps better analyze production data and enhance history matching calibration.
Post-processing technique using resistivity inversion model and mud filtrate resistivity allowed to calculate fractures and cleats aperture as well as porosity. Fracture and cleat properties, i.e. density, length, area, aperture and porosity, were inputs for permeability calculation using fracture permeability published equations. Also, a multilinear regression (MLR) model was built for permeability estimation by utilizing the burial depth and properties of fractures and cleats.
The coal seam obtained modelled permeability is a continuous curve capturing vertical heterogeneity in both qualitative and quantitative ways. The MLR model improved the match compared to the published permeability equations. Results show that the modelled MLR permeability perfectly matches the actual diagnostic fracture-injection/falloff test (DFIT) and wireline formation tester mini-DST permeabilities. The study showed the role of petrophysical logging in evaluating coal permeability by minimizing the uncertainty associated with the traditional permeability estimation. The technique provides denser permeability points for the static model, significantly enhancing the model properties distribution. It is important to mention the benefit of this technique in selecting the best reservoir portion of the target seam during lateral wells planning.
