A logging-curve separation scale overlay method for total-organic-carbon estimation in organic-rich shale reservoirs

2017 ◽  
Vol 5 (3) ◽  
pp. T387-T398 ◽  
Author(s):  
Jingling Xu ◽  
Yangyang Li ◽  
Baoying Zhang ◽  
Lei Xu ◽  
Yuxing Qin
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Shale Gas ◽  
Evaluation Method ◽  
Gamma Ray ◽  
Quantitative Estimation ◽  
Well Logs ◽  
Reservoir Evaluation ◽  
Overlay Method ◽  
Shale Reservoirs

Quantitative estimation of total-organic-carbon (TOC) content using well logs is very important for shale-gas reservoir evaluation, especially when core data are limited. Even though many techniques have been developed to calculate TOC from well logs, they all have their limitations and accurate assessment of TOC in organic-rich tight shales is still challenging. We have used an improved log-overlay method for evaluating TOC content in organic-rich tight shale reservoirs by overlying the properly scaled gamma-ray, sonic transit time, and bulk density curves on top of the compensated neutron log (CNL) curve based on the same concept to the original [Formula: see text] technique. These logging curves are overlapped with the CNL in nonsource intervals, and they are separated in organic-rich shale reservoirs. The separation magnitude increases along with the increase in TOC content of the shale, and this relationship is transformed to calculate TOC. This method was tested and verified by doing a case study using well-log data from the Jiaoshiba tight shale-gas play in the Sichuan Basin. The results illustrated that the new TOC evaluation method is more practical and effective compared with existing TOC evaluation methods.

Logging Evaluation Method and its Application for Measuring the Total Organic Carbon Content in Shale

2014 ◽  
Vol 962-965 ◽  
pp. 51-54
Author(s):  
Zhi Feng Wang ◽  
Yuan Fu Zhang ◽  
Hai Bo Zhang ◽  
Qing Zhai Meng
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Shale Gas ◽  
Sichuan Basin ◽  
Evaluation Method ◽  
Organic Carbon Content ◽  
Geochemical Analysis ◽  
Data Logging ◽  
Longmaxi Formation ◽  
Sedimentary Structures

The acquisition of the total organic carbon (TOC) content mainly relies on the geochemical analysis and logging data. Due to geochemical analysis is restricted by coring and experimental analysis, so it is difficult to get the continuous TOC data. Logging evaluation method for measuring TOC is very important for shale gas exploration. This paper presents a logging evaluation method that the shale is segmented according to sedimentary structures. Sedimentary structures were recognized by core, thin section and scanning electron microscope. Taking Wufeng-Longmaxi Formation, Silurian, Muai Syncline Belt, south of Sichuan Basin as research object, the shale is divided into three kinds: massive mudstone, unobvious laminated mudstone, and laminated mudstone. TOC within each mudstone are calculated using GR, resistivity and AC logging data, and an ideal result is achieved. This method is more efficient, faster and the vertical resolution is higher than △logR method.

Predicting Pyrite and Total Organic Carbon from Well Logs for Enhancing Shale Reservoir Interpretation

2012 ◽  
Author(s):  
James M. Witkowsky ◽  
James Elmer Galford ◽  
John Andrew Quirein ◽  
Jerome Allen Truax
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Well Logs ◽  
Shale Reservoir

scholarly journals Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Osama Siddig ◽  
Ahmed Farid Ibrahim ◽  
Salaheldin Elkatatny
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
The Other ◽  
Well Logs ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Average Error ◽  
Support Vector ◽  
Empirical Correlations

Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δt), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), uranium (Ur), and potassium (K). Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods’ parameters were tested to assure the best possible accuracy in terms of correlation coefficient (R) and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with R values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions.

Direct TOC Quantification in unconventional Kerogen-rich Shale Resource Play from Elemental Spectroscopy Measurements: A Case Study from North Kuwait

2015 ◽  
Author(s):  
Girija K. Joshi ◽  
Mihira N. Acharya ◽  
Marie Van Steene ◽  
Sandeep Chakravorty ◽  
Christophe Darous ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Gamma Ray ◽  
Horizontal Wells ◽  
Final Analysis ◽  
Total Carbon ◽  
Empirical Estimation ◽  
New Type ◽  
Borehole Image ◽  
Borehole Image Logs

Abstract The deep organic-rich calcareous Kerogen of North Kuwait, a continuous 50ft thinly alternating carbonate – organic-rich argillaceous sequence, is not only a source rock but has gained importance as potential reservoirs themselves of typical unconventional category. Resource play or Kerogen characterization relies on quantifying total organic carbon (TOC) and estimating accurate mineralogy. This paper describes the first attempt to directly measure total organic carbon of the Limestone-Kerogen sequence. For the present study, empirical estimations of TOC have been carried out based on conventional log measurements and nuclear magnetic resonance (NMR). The introduction of a new neutron-induced capture and inelastic gamma ray spectroscopy tool using a very high-resolution scintillator and a new type of pulsed neutron generator for the deep unconventional kerogen resources have provided a unique opportunity to measure a stand-alone quantitative TOC value using a combination of capture and inelastic gamma ray spectra. In this process, Inorganic Carbon Content (ICC) is estimated by using elemental concentrations measured by this logging tool in addition to measuring Total Carbon, and this value is subtracted from the measured total carbon to give TOC. The advanced elemental spectroscopy tool measurements were first used to determine accurately the complex mineralogy of the layered carbonate and organic-rich shale sequence. Extensive laboratory measurements of core / cuttings data were used to calibrate the petrophysical evaluation and capture the heterogeneity seen on borehole image logs. The final analysis shows considerable improvements compared to conventional empirical estimation. Once the mineralogy is properly determined, the log-derived TOC matches very well with core measured TOC. This technique has provided a new direct and accurate log-derived TOC for Kerogen characterization. The application has a potential to be used for CAPEX optimization of the coring in future wells. This technique can also be applied in HPHT and High-angle horizontal wells, which can overcome challenging coring difficulties in horizontal wells.

scholarly journals Evaluation method for resource potential of shale oil in the Triassic Yanchang Formation of the Ordos Basin, China

2020 ◽  
Vol 38 (4) ◽  
pp. 841-866
Author(s):  
Qiulin Guo ◽  
Xiaoming Chen ◽  
Xiaoxue Liuzhuang ◽  
Zhi Yang ◽  
Man Zheng ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Calculation Method ◽  
Evaluation Method ◽  
Ordos Basin ◽  
Oil Yield ◽  
Shale Oil ◽  
Recovery Coefficient ◽  
Evaporative Loss ◽  
Total Oil

The widely distributed, thick Chang 7 Shale is the richest shale oil formation in China. A calculation method for the evaporative hydrocarbon recovery coefficient based on formation volume factor is proposed considering the correction of heterogeneity-based total organic carbon differences to improve the adsorbed oil calculation method, and light hydrocarbon evaporative sampling losses, which can make mobile and total oil calculations more accurate. The adsorbed oil, S1 evaporative loss, total oil yield, and movable oil yield of 200 shale samples from the Chang 7 Member were calculated using the new methods. Results show that S1 evaporative loss accounts for 29% of S1, total oil yield is 3.5 times S1, and movable oil yield accounts for 37% of total oil yield. Based on the calculated total oil yield and movable oil yield results, the relationships among total oil yield, movable oil yield, and total organic carbon of the Chang 7 were established yielding total oil yield and movable oil yield estimates of 11.12 × 109 t and 4.01 × 109 t, respectively, revealing its tremendous shale exploration potential.

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