Derivation of Stochastic Equations for Computational Uncertainties in Petro-Physical Properties Using a Simplified Algorithm

This paper presents a simple mathematical algorithm or procedure for computing the uncertainties at the various percent of data input, using the stochastic approach of simulating the input variables to compute the output variables. A simple algorithm was used to derive stochastic equations for some selected petrophysical parameters using the relative standard deviations techniques (σ). These equations also known as reference work equations were found to produce reasonably accepted magnitude of uncertainties in the different parameters associated with cores. Equations were derived for the percent uncertainties in the values of the pore volume of the core - Vp, the fluid saturation – Sw, Sor, the porosity of the core – Φ, formation factor - F, bulk density – ρB, the derived porosity - ΦL and the derived permeability – KBU, Kro, Krw. The uncertainty equations can also be used to define the maximum level of uncertainty that can be tolerated in any independent variable if the maximum uncertainty to be tolerated in the dependent variable is knownKeywords: Stochastic equations, petrophysical parameters, percent uncertainties, Mathematical algorithm, standard deviations, partial derivatives, degree of accuracy, empirical models

Prospect Evaluation of Hydrocarbon Reserves Using 3-D Static Modeling in D-Field Onshore, Niger Delta Basin Area

Qualitative and quantitative approaches are often adopted to characterize known reservoir in any given field. A 3-D static modeling has been used to have full understanding of the field of the reservoir uniqueness of the study area, D-field. The hydrocarbon bearing sounds of the field were modelled using 3-D seismic data of the field, integrating with the well log and checkshot data of the field. The stochastic model approach was adopted to distribute the rock properties (Structural and Petrophysical) into a 3D grid using Sequential Gaussian Simulation which identified fifteen (15) major faults across the reservoirs. Reservoirs R_3000 had average thickness of 123 m, net-to-gross of 65%, porosity of 26%, water saturation of 43%, permeability of 1570.649 mD, based on Rider’s classification the reservoir R_3000 shows a very good porosity and an excellent permeability. These values are satisfactory for economic production. The environments of deposition of the reservoirs based on log motifs are interpreted as distributary channel fill and shoreface of the porosity and permeability of D-Field are within the range of values reported in the Niger Delta. Stochastic volumetric analysis estimated that the reservoir of interest to contain a reserve of averagely 14245.50 MMSTB. Furthermore, the integration of these subsurface data (well log and seismic) has led to simulation of a consistent 3-D static model of the reservoir which very well serves as input into the dynamic simulation model, so that forecasting and other sensitivity analysis can be run to provide the basis for effective reservoir management and development strategy. Keywords: Stochastic model, Qualitative, Quantitative, Gaussian simulation, Static modeling, Volumetric DOI: 10.7176/JEES/12-3-05 Publication date:March 31st 202