Amplitude Supported Prospects, Analysis and Predictive Models for Reducing Risk of Geological Success

Abstract

Summary Direct Hydrocarbon Indicators (DHI) are commonly used for exploration prospects. Amplitudes as an independent source of information could be used as conditional probability within Bayes Theorem to assess risk of geological success. Following research is aiming to construct predictive model for estimating probability of hydrocarbons observing DHI, P(dhi|hc). In order to build such model, we used Rose & Associates DHI Interpretation and Risk Analysis Consortium database, which contains extensive descriptions of 336 drilled prospects, with known results, across various categories: Geology, Data Quality, Amplitude Characteristics and Pitfalls. Multiple Logistic Regression was used for predicting probability P(dhi|hc). Three methods were considered within the study: two data-driven models - stepwise regression and lasso shrinkage method plus the third one, a combination of data-and expertise- driven approach - stepwise regression plus manual addition of predictors to the model. All three models with key predictors are described and give similar accuracy of prediction − 77%. Performed data analysis and calculated models reveal several insights into R&A DHI Consortium database and amplitude prospects characterisation. The best method to create such models is probably a combination of data and expertise driven approaches, while selection of most appropriate model is a question of company's strategy.