Classification of Post Office Outlets According to Risk of Incident

Porosity, permeability and TOC prediction from well logs using a neural network approach
Alpana Bhatt^,1,2 and Hans B. Helle¹

1) Norsk Hydro, E&P Research Centre, N-5020 Bergen, Norway

2) Norwegian University of Science and Technology, Dept. of Petroleum Engineering and Applied Geophysics,

N-7034 Trondheim, Norway

Introduction

Apart from their obvious impact on reservoir flow patterns, porosity and permeability are the key variables for characterising the sediments in a basin-scale fluid migration analysis. For basin modelling involving quantification of hydrocarbon generation and its migration pathways, the organic content of the source rock is one of the key inputs.

Several relationships have been offered which can relate porosity to wireline readings such as the sonic transit time and density logs. However, the conversion from density and transit time to equivalent porosity values is not trivial. The common conversion formulas contain terms and factors that depend on the individual location and lithology; e.g. grain density and grain transit time for the conversion from density and sonic log, respectively, that in general are unknown and remain to be determined.

Permeability is also recognised as a complex function of several interrelated factors such as lithology, pore fluid composition and porosity. Thus, estimates from well logs often rely upon porosity; e.g. through the Kozeny-Carman equation that also contains adjustable factors such as the Kozeny constant that varies within the range 5-100 depending on the reservoir rock and grain geometry (Rose and Bruce, 1949). Prediction of organic carbon (TOC) from wireline logs using the DlogR technique of Passey et al. (1990) is also tied to model parameters that include an exponent (LOM) that characterise the source rock maturity.

Motivated by the results of artificial neural network (ANN) modelling by Huang and Williamson (1995, 1997) applied to porosity, permeability and TOC prediction from wireline data offshore Canada, we applied this new approach to data from the North Sea. Our results clearly indicate that from a high-quality standard log suite, the prediction of formation properties by ANN is simple to implement and, moreover, implies accuracy exceeding that of conventional conversion techniques based on semi-empirical formulas or linear regression analysis.

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