Prediction of directional grain size distribution: An integrated approach

Recent advances in grain size distribution modelling have tended to de-emphasize the conventional method of carrying out grain size distribution analysis. This is a direct result of the difficulties surrounding core acquisition, preparation and analysis. Huge rig time spent acquiring cores and additional time spent in the laboratory is also a major discouraging factor. In response to this trend, researchers have come up with models capable of grain size distribution analysis without any need to acquire cores and carry out analysis on them. This work is a sort of response to this new trend and a contribution to the research drive in this core area. A new technique for grain size distribution estimation has been developed. The new technique integrates both statistical and neural network methods. Statistical methods were used to develop sets of equations for estimating some percentile grain sizes such as d₁, d₂, d₁₆, d₈₄, d₉₈ and d₉₉ Each equation relates one of these percentile sizes with d50 and standard deviation or sorting. Neural network method was used to predict the median grain size (d₅₀). Krumbein and Monk and Bergs equations were transposed and used to estimate the standard deviation. The technique also has the functionality of resolving grain size distribution into horizontal and vertical components for sand failure analysis The technique is simple and is dependent on only petrophysical and textural information of porosity, permeability, irreducible water saturation and pack structure coefficient which are readily available from well logs and/or empirical models.