Dual-shale-content method for total organic carbon content evaluation from wireline logs in organic shale

Abstract

Organic shale is one of the most important unconventional resources all around the world. Total organic carbon (TOC) content is an important evaluation parameter of reservoir hydrocarbon source quality. The regular evaluation methods have higher requirements of well logs and core experiment data for statistical regression. Through analyzing the resistivity and gamma ray logging response characteristics of shale content and organic matters, combined with digital rock physics experiment simulation, we put forward and improve the dual-shale-content method for TOC content logging evaluation. The accuracy of this method is verified by actual data processing. The result shows the dual-shale-content method is simple to use and the error is small. And by comparing with the calculation results by using the ∆LogR method, it is revealed that the trend of the TOC content calculated by our method agrees with the core results better. This new method is suitable for the evaluation of TOC content in the area or interval where there is only conventional logs.

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

“… 39 Subsequently, conventional logs have been fed as inputs, that is, gamma ray log, density log, acoustic log, deep and medium resistivity logs, and porosity log in addition to uranium (U), thorium (Th), and potassium (K) contents. 42 − 44 X-ray fluorescence elements’ data (such as copper and nickel) and thermal neutron porosity 1 as well as conventional log combinations displayed correlation with the TOC in the investigated environment 45 − 50 and were proofing evidence of AI reliability in predicting TOC. Table 1 provides a summary of the studies conducted for predicting the organic matter in shale formations.…”

“…Artificial intelligence (AI) has been considerably used in the last few years in oil and gas research, and much work has been made on the prediction of TOC based on core and well log data. ,− In most cases, AI methods are not globally applicable due to the heterogeneity of shales, which indicates the cruciality of studying the nature of the targeted fields and picking proper logs for the model. , Huang et al demonstrated one of the early applications of AI in predicting TOC using only three conventional (gamma ray, resistivity, and sonic) logs as an input and a pseudo-TOC log calculated from an empirical correlation following the Passey et al approaches . Subsequently, conventional logs have been fed as inputs, that is, gamma ray log, density log, acoustic log, deep and medium resistivity logs, and porosity log in addition to uranium (U), thorium (Th), and potassium (K) contents. − X-ray fluorescence elements’ data (such as copper and nickel) and thermal neutron porosity as well as conventional log combinations displayed correlation with the TOC in the investigated environment − and were proofing evidence of AI reliability in predicting TOC. Table provides a summary of the studies conducted for predicting the organic matter in shale formations.…”

Oil Content Prediction Method Based on the TOC and Porosity of Organic-Rich Shales from Wireline Logs: A Case Study of Lacustrine Intersalt Shale Plays in Qianjiang Sag, Jianghan Basin, China

“…The TOC can be calculated by using well logging data with various methods, including the Δ log R method and dual-V sh method [26][27][28]. In this paper, we choose the dual-V sh model presented by Nie et al [26] because it has been proved efficient in the research area. The workflow of this method is as follows.…”

“…The formation bulk model was simplified into two parts: brine-bearing shale and organic-bearing shale (Figure 3) [26]. Because clay minerals have a stronger ability to absorb radioactive substances than other minerals, mudrock formations always show high values of natural gamma radiation in well logs.…”

Mechanism of Forming Low Resistivity in Shale Reservoirs

“…Nevertheless, few network models have been established for numerical simulation of various factors. Most shale gas reservoirs are featured by apparent anisotropy owing to their complicated pore structure [43][44][45][46][47]; furthermore, the existence of conductive substances makes the conductive mechanism extremely complex, leading to difficulties in the application of the Archie equation which is commonly used for logging interpretation. Quite a few resistivity studies focused on conventional gas reservoirs, while few models incorporating pore structure features have been established for qualitative analysis.…”