Enhancing Accuracy and Range of Sourceless Density
Formation density is a fundamental rock property and is a critical input in reservoir evaluations. Determining this critical measurement accurately requires utilizing a high-activity radioactive source in the drilling bottomhole assembly (BHA). The sourceless neutron-gamma density (sNGD) measurement based on a pulsed-neutron generator was introduced in logging-while-drilling (LWD) operations more than a decade ago, eliminating the need for radioactive source and the challenges associated with its handling, transportation, storage, and abandonment. The current sNGD algorithm has limitations in high-density formations, shales, overgauge boreholes, and heavy mud systems, which impact its accuracy. A new sourceless density algorithm has been developed based on recent advances in nuclear modeling and downhole hardware capabilities and provides a step-change improvement over the current algorithm. This new sourceless density algorithm brings the measurement accuracy across a wide range of formation types and drilling environments closer to that of the gamma-gamma density (GGD) that utilizes radioactive sources. This paper reviews the improvements in measurement accuracy brought by the detailed understanding of the physics of the measurement, thereby acting as an enabler to move away from the radioactive source in a high-risk drilling environment and where regulatory constraints exist while also reducing the environmental impact on the operation. The paper also explains the integration of sourceless density measurement with other measurements generated from the pulsed-neutron generator and demonstrates the impact through results on log data.
Authors
- Rene Rodriguez (ORCID: https://orcid.org/0000-0003-4878-0299)
- Vivek Agarwal (ORCID: https://orcid.org/0000-0002-7287-3455)
- Françoise Allioli
- Marie-Laure Mauborgne (ORCID: https://orcid.org/0009-0006-5688-6682)
- Viktoriya Sergeyeva
- C. Stoller
- Vikas Jain (ORCID: https://orcid.org/0000-0001-5110-6831)
- Alexis Pallain
- Marie Van Steene
- Karam Ali AlSadiq
- Fabien Harange
- Idris Babaghayou
- Erwan Tanguy
Publication Details
- Journal
- Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description
- Published
- 2024-12-01
- DOI
- https://doi.org/10.30632/pjv65n6-2024a7
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00