Rock Classification in the Haynesville Shale-Gas Formation Based on Petrophysical and Elastic Rock Properties Estimated From Well Logs
- Mehrnoosh Saneifar (Texas A&M University) | Alvaro Aranibar (Texas A&M University) | Zoya Heidari (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 30 September-2 October, New Orleans, Louisiana, USA
- Publication Date
- Document Type
- Conference Paper
- 2013, Society of Petroleum Engineers
- 4.3.4 Scale, 5.8.2 Shale Gas, 5.8.7 Carbonate Reservoir, 5.6.1 Open hole/cased hole log analysis, 4.1.5 Processing Equipment, 5.1 Reservoir Characterisation, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.1.1 Exploration, Development, Structural Geology, 1.2.3 Rock properties, 1.6.9 Coring, Fishing, 4.1.2 Separation and Treating, 5.2 Reservoir Fluid Dynamics
- Well-Log Interpretation, Organic-Shale Formations, Rock Classification, Elastic Properties, Petrophysics
- 5 in the last 30 days
- 807 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
Rock classification can enhance fracture treatment design for successful field development in organic-shale reservoirs. Rock brittleness and elastic properties are important parameters to be considered in selection of the best candidate zones for fracture treatment. However, characterization of the rapid variation of petrophysical, compositional, and rock elastic properties in organic-shale reservoirs is challenging. Well logs can be applied for real-time assessment of these formation properties at relatively high resolution compared to core measurements. In this paper, we introduce a rock classification technique in shale-gas reservoirs that takes into account well-log-based estimates of rock elastic properties as well as mineralogy, kerogen porosity, and organic-richness.
We first conduct a joint interpretation of well logs and core measurements to estimate depth-by-depth petrophysical and compositional properties of the shale-gas reservoir. The self-consistent approximation model is applied to estimate rock elastic properties. This model enables the assessment of elastic properties based on the estimated mineralogy and the inclusion shapes. We then calculate rock brittleness index from the estimated elastic properties. Rock classes are determined using estimated rock brittleness, mineralogy, total organic content. Finally, kerogen porosity is incorporated in the rock classification to further improve the identified rock types.
We applied the introduced rock classification technique in the Haynesville shale-gas formation. The identified rock classes were cross-validated with the existing core images. Unlike core-based rock classification techniques in organic-shale reservoirs, the introduced method in this paper is mainly based on well logs and can provide reliable rock classes without requiring a large core database. An efficient well-log-based rock classification technique can potentially enhance fracture treatment and production from complex organic-shale reservoirs through (a) detecting the best candidate zones for fracture treatment and (b) optimizing the number of required fracture stages.
|File Size||1 MB||Number of Pages||12|