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Drill hole optimisation using geostatistics has been used in many parts of the world in respect of various mineral deposits including coal. The present paper is an attempt to provide a means to maximise the information to cost ratio for optimisation of exploration drilling in the Jharia coalfield, which is the only source of prime coking coal in India. Exploration in Jharia Coalfield that has led to the generation of a huge exploration database has been carried out by various governmental and non-governmental agencies. But because of lack of importance and an obvious application towards exploration modelling, the database had not been utilised to its fullest extent. In the present study, an attempt has been made to derive geostatistical models of various proximate coal constituents of selected seams in respect of nine opencast mine blocks of the coalfield. This experimental study, employing geostatistical volume-variance relationships, revealed that an exploration grid density of 16 drill holes per square kilometre with a grid spacing of 3006300 m within an exploration area of 161 km is adequate for maximisation of information in respect of the Jharia Coalfield. Until now, the density of exploration drilling in the coalfield has been considered as per Indian Standard Procedure (ISP) norms solely based on past experience and proposed mining methods. The present study of exploration drill hole optimisation using the concept of kriging variance is associated with an objectively defined mathematical basis and therefore may be applied for exploration optimisation in other coalfields in India and other countries, which occur in similar geological settings.

In an unconventional reservoir, a thorough understanding of the spatial distribution of the physical properties of rocks, in terms of facies, porosity, and permeability, is essential for realistic dynamic reservoir simulation and history matching. This paper provides a practical solution for enhanced reservoir performance analysis, combining the results of geological interpretation, 3D geostatistical electrofacies modeling, and flow simulation in an unconventional Eagle Ford shale play. The first step of the integrated approach is the application of hierarchical clustering methods to identify electrofacies groups using log curves. Next, electrofacies are converted into lithofacies through an analysis of core data. The 3D lithofacies and petrophysical distribution model is then created using stochastic geostatistical techniques. In the reservoir simulation step, the discretized facies model is constrained to assign geomechanical properties. Thus, a realistic fracture model is generated with a proper definition of fracture characteristics to control flow simulation and to enable better history matching. The solution presented in this paper provides an objective means of using the integrated approach in an accurate definition of fracture properties, in terms of length and orientation, for reservoir simulation and production forecasting in unconventional reservoirs.

In mature field appraisal and development, discretized geomechanical property models play a vital role in providing information on in situ stress regime as a guide for placement of directional wells. Laboratory methods of measuring these properties, in most cases, take only small samples from consolidated rocks. These isolated samples may not be representative of the entire elastic regime existing in the reservoir owing to sample size. In general, geomechanical studies are performed on a well-by-well basis and then these measurements are used as calibration points to convert 3D seismic data (if available) to geomechanical models. However, elastic properties measured this way are restricted to the well location and interpolation across the reservoir may not be always appropriate. To overcome these challenges, this paper describes an integrated approach for deriving 3D geomechanical models of the reservoir by combining results of 3D geocellular models and basin models. The basin model reconstructs the geologic history (i.e., burial history) of the reservoir by back-stripping it to the original depositional thickness. Through this reconstruction, the mechanical compaction, pore pressures, effective stress, and porosity versus depth relationships are established. Next, these mechanical properties are discretized into 3D geocellular grid using empirical formulas via lithofacies model even if no 3D seismic data are available for the reservoir. The discretization of elastic properties into 3D grids results in a better understanding of the prevailing stress regimes and helping in design of hydraulic fracturing operations with minimal risks and costs. This approach provides an innovative way of determining effective horizontal stress for the entire reservoir through 3D distribution of elastic properties.