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Abstract

Summary

The estimation of shale components is important for the study of shale oil and gas exploration and development. This paper introduces a method (SCP-BPlogR) to evaluate shale components using conventional well logs. The SCP-BPlogR model is a method by combining BP neural network with the improved ΔlogR technique. In the work, the component model of shale is firstly constructed and calibrated, and then the principles of the SCP-BPlogR model is reviewed, including the brief introduction, kerogen volume calculated, well logs selected and the BP neural network training. Finally, this paper documents a case study from the Paleogene Shahejie Formation shales in Damintun Sag of Bohai Bay Basin where the SCP-BPlogR is successfully implemented and is in good agreement with core measurements. SCP-BPlogR model is a non-linear method, which pays attention to the influence of organic carbon in residual oil and gas on kerogen volume and the effect of kerogen on mineral volume and porosity. The field example confirm the accuracy and reliability of the SCP-BPlogR model to estimate shale components.

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/content/papers/10.3997/2214-4609.201701270
2017-06-12
2024-04-20

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References

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A New Method to Evaluate the Shale Components From Conventional Logs
Conference Proceedings 2017, 1 (2017); https://doi.org/10.3997/2214-4609.201701270
/content/papers/10.3997/2214-4609.201701270
/content/papers/10.3997/2214-4609.201701270
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