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Abstract

Summary

Nowadays Low Resistive Low Contrast pays (LRLC) are forefront of mature-field secondary objectives where production from conventional reservoirs get lesser and lesser. Although LRLC reservoirs have been under production for many years, knowing reservoir complexity mainly at volume and flow capacities are still big challenges. Lithology is one of the element, which can carry some portion of reservoir complexity. Among the possible scenarios, shale plays a different role; Understanding the shale behavior and its distribution patterns is a key to improve the reservoir characterization and consequently unlock potential unseen volumes. In the absence of a well defined distribution model, an integrated approach of forward modeling and inversion is also impractical for the accurate evaluation. Majority of conventional approaches unable to address reservoir complexity due to log resolution constrains. This paper aims to introduce new approach, as a tool, to qualitatively determine thin-bedded sand characteristics, which can be used as an integral part of low-resistive techniques. It implements ideal resistivity-base model (RT-Model) to evaluate shale distribution in clastic reservoirs. The process couples the deep resistivity with gamma ray measurements in new laminar gauge to properly determine not only corrected shale volume but also distribution pattern independent of advance log measurements. Based on the result the method is able to quantify bed thickness smaller than a feet (2 to 4 in) that is actually beyond conventional log resolution. Corrected shale volume and sand resistivity are main products while net sand porosity and fluid saturation are secondary products derive through workflow optimization practices and uses inputs from new lamination model. Resistivity-base porosity shows better consistency with core data where traditional approach failed to match core trend in highly laminated section.

Outcomes has been finally verified with hard evidences and direct measurements like image log, production profiles and core data. Compression shows encouraging results at both well and field scales. At well level, RT-Model products has been fully align with direct measurements and at field scale, results have been fully supported by dynamic model where enhanced volume and flow capacity from RT-Model are closely tied with expectation from the model.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201803256
2018-12-03
2024-04-27

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References

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State Of The Art In Petrophysical Modeling Of Low Resistive Low Contrast Pays (Lrlc) Using Ideal Resistivity Model
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201803256
/content/papers/10.3997/2214-4609.201803256
/content/papers/10.3997/2214-4609.201803256
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