Integrating petroelastic modeling, stochastic seismic inversion, and Bayesian probability classification to reduce uncertainty of hydrocarbon prediction: Example from Malay Basin

Exploring hydrocarbon in structural-stratigraphical traps is challenging due to the high lateral variation of lithofluid facies. In addition, reservoir characterization is getting more obscure if the reservoir layers are thin and below the seismic vertical resolution. Our objectives are to reduce the uncertainty of reserve estimation and to predict hydrocarbon distribution more accurately in such reservoir layers by proposing a new workflow that works better than the conventional one. The approach was performed by integrating petroelastic modeling, stochastic elastic seismic inversion, and Bayesian probability classification in the upper reservoir layer of Group E in the Northern Malay Basin. A robust petroelastic model was initially built to obtain more obvious separation of different lithofluid classes in elastic properties crossplot, that is acoustic impedance versus [Formula: see text] ratio. To achieve reliable distribution of elastic properties per identified lithofluid class, a Monte Carlo simulation was then run and the posterior probability of all classes was computed using Bayesian classification, followed by confusion matrix assessment. Stochastic elastic seismic inversion was carried out on conditioned seismic data to predict elastic properties away from the wells. Using all elastic properties realizations, ranking was calculated and uncertainty was quantified at the blind well location. The most probable scenario is the realization that has a much closer probability to the measured criterion value at the blind well. The computed posterior probability of hydrocarbon-bearing sand was applied on the selected stochastic realization (acoustic impedance and [Formula: see text] volumes) according to the ranking result. Finally, the hydrocarbon distribution probability map was generated and validated with lithofluid facies information of four distributed wells. Such a comparison authenticated the hydrocarbon prediction particularly at the blind well location.

Uncertainty Analysis of Two Methods in Hydrocarbon Prediction under Different Water Saturation and Noise Conditions

The uncertainty of two recently proposed methods, “new fluid factor” and “delta K”, is analyzed under different water saturation and noise conditions through Monte Carlo modelling. The new fluid factor performs reliably (all metric parameters are above 0.9) when the water saturation is up to 95%. The delta K has better performance (all metric parameters are close to 1) such that it is able to distinguish hydrocarbon from brine without the interference of high water saturation. The results prove the performances of the two methods are stable in a high water-saturation scenario. The analysis of noise indicates the methods are sensitive to noise in the input data in that the performance is excellent when the noise is relatively low (−20 dB) and decreases with increasing noise energy. The new fluid factor, which is in the interface domain, is more sensitive than delta K in the impedance domain. The metric parameters of the new fluid factor and delta K are in the range of 0.5 to 0.8 when the noise is high (−7 dB). High-quality input data and integration with other geophysical methods can effectively reduce these risks. In addition, two widely used traditional methods (fluid factor and Lambda-Rho) are analyzed as comparisons. It turns out the new fluid factor and delta K have better performance than traditional methods in both high water saturation and noise conditions.

THE THEORETICAL-EMPIRICAL TECHNIQUE OF HYDROCARBONS PREDICTION IN THE BOREHOLES LOGS WITH THE BASIC PARAMETER – COMPRESSIBILITY

The theoretical-empirical technique of hydrocarbon prediction in the boreholes logs is presented. It is based on the adequate physical-mathematical model of rocks, on the empirical relations between compressibility, porosity and pressure for these rocks, on the core data and also on acoustic logging data (the interval times or body wave’s velocities). For the cases of the acoustic logging data absence, the variants of the prediction technique by using data of gamma-logging, electric logging, and the offset logging method are developed. The presented technique is realized as a system of theoretical and empirical relations and the resulting functional. The adequate set of software tools is developed in the Fortran, C# and Excel environments. The technique is tested on the well’s data of a number of structures of Western oil and gas region of Ukraine: Lishchynska, Buchatska, Ludynska, Zaluzhanska, Zarichnianska, Nyklovytska, Orkhovytska. The statistical estimations of petrophysical characteristics of rock-collectors of those wells are presented. For more reliable prediction by the technique, instead of relations for the general parametric base, the empirical relations for concrete available in the studied boreholes logs types and subtypes of rock-collectors are elaborated.