As an unconventional gas resource with huge potential, shale gas is currently popular in petroleum exploration and production. The total organic carbon (TOC) content is one of the most important parameters for evaluating shale quality and hydrocarbon potential. The earliest way to evaluate TOC content was measuring from core samples directly in the laboratory; however, this method depended significantly on variations within the core samples, and the result profile wasn’t continuous. Organic shale formations respond to gamma ray, porosity and resistivity logs differently than the surrounding rocks and wireline logs started to be used to evaluate the source rock potential [1–4]. The wireline log-based TOC content evaluation can provide continuous and in-situ results of the target formation.
TOC evaluation methods based on wireline logs have been studied by previous researchers. Gamma-ray spectral logs were first introduced to identify organic-rich rocks and their relationships with total gamma ray, uranium, and thorium-potassium ratio were analyzed by Fertl and Rieke (1980)  and Fertl and Chillngar (1988) . Then the density log was used to estimate TOC content widely since Schmoker and Hester (1983)  proposed the technology. Mendelzon and Toksoz (1985)  found quantitative relationships between wireline logs and the TOC from core samples by using multivariate methods; the regression equation also had a high coefficient of determination. Passey et al. (1990)  proposed the ΔLogR technique by overlaying porosity logs (e.g., sonic, density, and neutron) and resistivity logs to calculate the TOC content, and it has become the most popular TOC estimation method that uses wireline logs in the last 2 decades. Many researchers used and improved the ΔLogR technique [2, 10–13]. However, when we use this method, we have to select a baseline manually, and the TOC background level varies regionally and is impossible to determine without core samples. Intelligent systems and neural networks have been applied to wireline log TOC predictions [14, 15]. However, neural networks are complex and involve many parameters which is difficult to choose. Therefore, in most cases, TOC estimation is achieved by constructing simple or multivariate regression methods . Tan et al. (2015) applied support-vector-regression machine technology in TOC estimation and acquired good results, and this technique needs multiple inputs .
In recent years, some direct in situ TOC estimation techniques have been introduced, including spectrum geochemical logging. Such technology could provide direct TOC measurements without requiring complex fitting algorithms [17, 18]. However, it’s expensive and only applied in a few wells.
In China, we have started to reinvestigate the oil shale formations in old wells to find out the shale gas source situation. Many old well log data are being reviewed to investigate the shale gas potential, thus a simple method to estimate the TOC with only a few logs is needed . In this study, we intend to calculate the TOC content by using the difference of shale content (Vsh) calculated by gamma ray (GR) and resistivity logs (Rt) which are available in most wells.
2.1 Formation model
As a kind of argillaceous hydrocarbon source rocks, oil shale has the properties of both source rock and mud stone. Former researchers always assume the organic matters as a part of the solid matrix of shale. In fact, the density of organics is as low as pore fluid, thus the responses to density wireline logging are more similar to the fluids. Additionally, according to the numerical simulation done by Nie et al. (2016) , the organics affect the electrical conductivity only through the cut of the current routine by their spatial distribution in shale formations. Therefore, we can assume the organics as solid oil which distributes in the pore spaces of oil shale. Then the organic-rich shale can be divided in three parts: rock matrix, organic matters and pore fluids . The volume models of shale rock can be simplified into three conditions as shown in Figure 1: (a) water bearing shale, (b) organics bearing shale and (c) water-organics bearing shale. These models will be used to analyze the conductivity of oil shale.
2.2 GR model for calculating Vsh
For the clay minerals have stronger ability to absorb the radioactive substance than the other minerals, the mud rock formation always show high value of natural gamma ray well logs. Therefore, the Vsh can be calculated by gamma ray logs. The Vsh acquired this way only concerns about the radioactivity of the formation, and it barely has nothing to do with the brine or organics. And this Vsh can be seen as whole shale content of the formations. The classic formulas to calculate the Vsh with gamma ray logs are as follows: (1) (2)
Where the SH is the original shale content, GRmin and GRmax are respectively the GR values of sand stone and pure mud stone formation. The gcur is the correction coefficient, which is 2 in the old strata and 3.7 in the new strata.
In fact, due to the strong ability of the organics to absorb radioactive substances, according to the formation models in Figure 1(c), the Vsh calculated from GR logs actually contains the volumes of shale bearing both brine and organic.
2.3 Rt model for calculating Vsh
The electrical conductivity of shale rock depends on the ions in the brine. Nie et al. (2016)  used digital core technology to simulate the conductivity of shale formation, and the relationship between the clay content and Rt was revealed (as shown in Figure 2). According to the simulation results, under ideal conditions, the content of brine bearing shale (Vshw) and the affective resistivity (Reff ) can be written as Eq. 3: (3)
Where c is the resistivity of pure shale formations, which is always be set as the Rt value of the formation which has the highest shale content; d is an index which is related to the conductivity of clay minerals, gas-bearing condition and porosity, and its value varies between 1∼2.
From Eq. 3, we can deduce the Vsh calculation formula with Rt logs: (4)
Vshw is the brine bearing shale content (as shown in Figure 1(a)).
2.4 Dual-Vsh model for calculating TOC content
After we calculate the Vsh and Vshw, the organic-bearing shale content (Vsho) can be deduced with Eq. 5 as follows: (5)
Then the relative volume content of TOC (simplified as TOC in Eq. 6) in oil shale formation can be calculated by multiplying the Vsho to the apparent porosity of the formation (φtsh). (6)
This is the final equation to calculate TOC content by using dual-Vsh method.
However, the calculation results always rise higher than the core TOC data where the TOC content is high. It’s because we haven’t consider the effect of the shale gas in the pore to the resistivity, thus we deduce higher Vsho by using Rt log . Therefore, we introduce a new parameter φcorr to in order to correct this effect. The φtsh can be corrected by the equation below: (7)
3 Application result and discussion
We have some wireline logs and core TOC data of a section in Well X, which is a shale gas well in Sichuan Basin, China . We calculated the TOC content by using the dual-Vsh method, and the result is shown in Figure 3. In Figure 3, the first track is depth; the second track contains Rt and GR logs; the third track shows the dual shale content, and where there is filled with color represent Vsho; the fourth track shows Rt and acoustic transit time (AC) logs which are used in ΔLogR method; the fifth track shows the TOC content calculated by using the dual-Vsh method and core TOC; the sixth track shows the TOC content calculated by using the ΔLogR method and core TOC.
From Figure 3 we can see that in the whole depth section, the TOC calculation result by using dual-Vsh method agrees with core TOC very well, including the two peak values in 1180 m and 1194 m where the result by using the ΔLogR method doesn’t. This result proved the efficiency to evaluate the TOC content by using this dual-Vsh method.
From the above study, a dual-Vsh method can be used to predict the hydrocarbon potential of a reservoir using wireline logs; also, this method is accurate and more flexible than using empirical formulas or the ΔLogR method.
However, there are uncertain factors while using this method. In different area, the parameters used may vary. And in some formations with high content of electrical conductive minerals such as pyrite, the shale content calculated from Rt will be affected very badly and the result may be tremendously different from the real condition.
This study primarily investigates how to use two normal wireline logs, GR and Rt, to estimate TOC contents. From this study, some conclusions can be drawn as follows:
In this study of the dual-Vsh method, by construct the models considering the organics distributing in the pores, the Vsh, Vshw and Vsho are separately calculated. By divide the porosity part of Vsho, we can deduce the TOC content.
We can get a better result in the high TOC content intervals if the gas’s effect on resistivity need is corrected by using a parameter φcorr.
In a case study, the comparison of TOC prediction by using our method and the core TOC shows the dual-Vsh method is accurate and efficient.
This method is suitable for the evaluation of TOC content in the area or interval where there is only conventional logs. The old wells of many oil fields in China only have some normal wireline log data but no core data or advanced logs. Because of only two regular logs are needed by using this method, it can help us dealing with the old well organic shale reinvestigation process.
While using this method, the conductive minerals can cause huge error which need to be considered.
We greatly appreciate the editors and two anonymous reviewers for their valuable and constructive comments and suggestions which helped us to improve our manuscript. This research is supported by National Natural Science Foundation of China (grant no. 41504094) and Open Fund of Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education of China (grant no. K2015-06).
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Published Online: 2017-05-05