Abstract
Conventional openhole log evaluation is typically done in a deterministic approach which leads to inherent uncertainty around the derived results based on the fixed assumptions made. This has historically stemmed from the fact that most of the porosity and saturation models utilized in the deterministic work flow were formulated long before the availability of computing processing power. With the advent of technology, statistical methods designed for quantitative formation evaluation of open-hole logs are now easily applicable.
Probabilistic log evaluations are generally done by solving simultaneous equations described by one or more interpretation models. Input log measurements and response parameters are used together with response equations to compute volumetric fractions results for formation components (minerals and fluids alike).
The system of equations built to conduct a volumetric analysis comprises tool parameters, minerals and fluid volumes and the tool responses parameters. The probabilistic workflow uses error minimizing or probabilities to solve a set of over-determined equations for the "best" answer. To achieve an over determined case, constraint equations are often imposed. Logging vendors usually offer this product as part of their service and software applications.
This paper analyses the typical workflow governing probabilistic evaluation methodologies and proposes a Python script based approach that enables the user to run a fast and simple mineral components evaluation based on porosity and basic input logs.
Data from a typical Niger Delta well is used to evaluate the workflow and the results are compared with a deterministic evaluation to see the added benefits.
