Net-To-Gross Ratios: Implications in Integrated Reservoir Management Studies

Abstract

Geoscientists and reservoir engineers are very well acquainted with the terms "net-to-gross (NTG)" and "petrophysical cut-offs" or just "cut-offs". The significance of these terms is ultimately to define productive zones in the reservoir for hydrocarbon exploitation. However, there has been marked misunderstanding surrounding the usage of the term, net-to-gross and implicitly, the derivation of cut-offs which are limiting values used in expressing the net-to-gross ratio. While the geologist may be concerned with the pay for evaluating hydrocarbon-in-place and the ultimately the estimation of economically producible reserves, the simulation engineer is more concerned with fluid flow for pressure support in the reservoir. The significance of this paper is to consider the different cut-offs selection methods, the varying interpretation of net-to-gross ratios and the implications inherent in such methods. A case study from a field in the Niger Delta is also carried out to reveal the impact of such selection criteria especially in rocks with congenital weak hydraulic properties which cannot be excluded at geologic correlation stage. The effect of cut-off parameters on oil-initially-in-place (OIIP) calculation was also investigated and a sensitivity analysis carried out on the petrophysical parameters to reveal the impact of the dynamically conditioned cut-offs selection on the petrofacies. Multiple Monte Carlo realizations were also employed to obtain probabilistic OIIP estimates rather than a single deterministic result. Introduction Over the decades, the term net-to-gross and by extension, cut-offs have been extensively used in the oil industry especially by petrophysicists, reservoir geologists, and reservoir/simulation engineers. It is a basic reservoir parameter used for solving various problems ranging from OIIP calculations to reservoir flow simulation as one among several other variables to determine and predict reservoir performance. However there has been a great deal of confusion arising from the ways these parameters are defined, derived, and used. The fact actually is that it actually depends on the professionals involved, the circumstance of their usage, and the intended deliverables. Therefore, there have been no clear-cut definitions for these terms since most investigators do not explain the methodology involved in arriving at their own results. These have led to errors in areas of volumetric calculations, recovery factor calculations, welltest interpretations, stimulation design, and equity determination among others.

Spatial and temporal distribution of hydrocarbon production on the UK continental shelf

“…NTG is defined as the proportion of gross rock volume to formation thickness hosting hydrocarbon fluids. This parameter is a key part of the calculation of in-place volume and is also a key component of reservoir heterogeneity which in turn impacts the economically recoverable reserves (Egbele et al, 2005). Data for NTG is drawn from a variety of peer reviewed publications covering the large number of fields.…”

A Method for Judging the Effectiveness of Complex Tight Gas Reservoirs Based on Geophysical Logging Data and Using the L Block of the Ordos Basin as a Case Study

“…Considering the abundance of logging data in actual oil and gas fields and the characteristics of high vertical resolution, high performance-price ratio, and reliability of logging data, which can lay the data foundation for parameter calculation, the validity identification of reservoirs in oil and gas reservoirs is mostly carried out by determining the lower limit of physical parameters, such as the effective porosity of effective reservoirs, and then combining this with logging data for parameter calculation. Previously, most scholars used porosity-permeability crossplots to determine the lower limit of the physical properties of the effective reservoirs [13,[17][18][19], which is simple, convenient, clear, and intuitive, but due to the coupling of physical parameters, especially permeability, the evaluation accuracy of this parameter is limited, and the practical use effect in the application process is not satisfactory. Wang et al improved this type of physical property method by classifying the pore throat structure through piezometric data, establishing the relationship between permeability and porosity separately, and then evaluating the lower limit of the physical properties of effective reservoirs [16], and this method improved the accuracy of the porosity-permeability crossplot method.…”