The Direct Measurement of Carbon in Wells Containing Oil and Natural Gas Using a Pulsed Neutron Mineralogy Tool

Abstract

The assessment of reservoir productivity and subsurface hydrocarbon can be significantly enhanced through an understanding of formation mineralogy and organic carbon. Such information allows petrophysicists to resolve ambiguities in their predictions of reservoir hydrocarbon potential. While core samples are a prime source for exact formation mineralogy, excellent results can also be derived in a timely and cost-efficient manner from in-situ log chemistry measurements of the rock. A direct measurement of the formation's elemental concentrations is achieved using a gamma ray scintillation sensor in combination with a 14-MeV pulsed-neutron generator. The most important element measured is carbon, as it may provide a direct indication of reservoir hydrocarbons. This paper presents a method for determining the amount of organic carbon in subsurface formations using a pulsed-neutron mineralogy tool and a natural gamma ray spectroscopy tool. The natural, inelastic, and capture gamma ray energy spectra from these instruments are used to extract the chemistry of the subsurface formation being investigated. The elemental concentrations measured include Al, C, Ca, Fe, Gd, K, Mg, S, Si, Th, Ti, and U. Carbon is very difficult to measure without the inelastic spectrum generated from a pulsed-neutron source. An interpretation process, based upon the geochemistry of petroleum-bearing formations, is employed to derive the lithology and mineralogy which leads to the interpretation of the carbon measurement. The oil saturation can be computed in conventional reservoirs, assuming that the amount of carbon in excess of that required for the inorganic matrix mineralogy is part of the pore fluid as hydrocarbon. The direct carbon measurement is also important in laminated shaly sands or in low-salinity reservoirs, where oil saturation determination from indirect measurements, such as resistivity, is not compatible with the environment. In other formations the carbon can be determined to be a component of the rock matrix as kerogen or coal, both of which are uniquely identified with this logging system. Kerogen becomes extremely important in the evaluation of shale gas formations. Field examples are presented to illustrate the effectiveness of the carbon measurement. Introduction Subsurface organic carbon, i.e., carbon that does not belong to any of the carbonate minerals, indicates the presence of oil, natural gas, coal, or kerogen. Although the amount of carbon is one of the most important quantities in formation evaluation, openhole tools often provide only indirect measurements of hydrocarbons. Traditional electrical tools, for example, measure oil saturation indirectly based upon a comparison of the resistivity of saline and non-saline formation fluids. This approach works best when the salinity of the formation water is high to moderate; if connate water salinity is low, resistivity methods cannot differentiate water from hydrocarbon.

Resistivity Independent Hydrocarbon Saturation Computation across a Lithologically Complex Jurassic Carbonate reservoir, Offshore Abu Dhabi

“…Total inorganic carbon fraction of the measured total carbon contributed by the rock matrix, is calculated from the weight fractions of all carbon bearing inorganic minerals, predominantly carbonates in the formation, where the coefficients represent the weight fraction of carbon in the respective carbonate mineral. Total organic carbon is then computed as the difference between the total carbon and total inorganic carbon (Pemper et al 2009):…”

State-of-the-art Openhole Shale Gas Logging

“…However, the accuracy of these empirical approaches is often reduced by the variable mineralogy present within a shale reservoir Pemper et al 2009). Several methods to determine the amount of TOC present have been developed by the industry using empirical relationships to various conventional log responses such as resistivity, bulk density and total gamma ray.…”

Total Organic Carbon Content Prediction in Lacustrine Shale Using Extreme Gradient Boosting Machine Learning Based on Bayesian Optimization

“…In recent years, the emergence of special logging methods has provided many approaches to predict TOC content. Examples include calculating the TOC content using element capture spectroscopy logs [21] or combining the nuclear magnetic resonance logs and the DEN log to estimate TOC content [22]. All the above methods are developed based on the rock physical model (RPM) and rely extensively on empirical formulas.…”