Seismic reservoir characterization of Duvernay shale with quantitative interpretation and induced seismicity considerations — A case study

2017 ◽  
Vol 5 (2) ◽  
pp. T185-T197 ◽  
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
Amit Kumar Ray ◽  
Hossein Nemati ◽  
Ray Morin ◽  
...  
Keyword(s):  
Media Coverage ◽  
Induced Seismicity ◽  
Oil And Gas ◽  
Reservoir Characterization ◽  
Learning Experience ◽  
Rock Physics ◽  
Quantitative Interpretation ◽  
Gas Generation ◽  
Gaseous Hydrocarbon

The Devonian Duvernay Formation in northwest central Alberta, Canada, has become a hot play in the past few years due to its richness in liquid and gaseous hydrocarbon resources. The oil and gas generation in this shale formation made it the source rock for many oil and gas fields in its vicinity. We attempt to showcase the characterization of Duvernay Formation using 3D multicomponent seismic data and integrating it with the available well log and other relevant data. This has been done by deriving rock-physics parameters (Young’s modulus and Poisson’s ratio) through deterministic simultaneous and joint impedance inversion, with appropriate quantitative interpretation. In particular, we determine the brittleness of the Duvernay interval, which helps us determine the sweet spots therein. The scope of this characterization exercise was extended to explore the induced seismicity observed in the area (i.e., earthquakes of magnitude [Formula: see text]) that is perceived to be associated with hydraulic fracture stimulation of the Duvernay. This has been a cause of media coverage lately. We attempt to integrate our results with the induced seismicity data available in the public domain and elaborate on our learning experience gained so far.

A Revised Gassmann Fluid Replacement Model Based on Neutron-Density Logs in Shaly Sandstone Reservoirs

2021 ◽  
pp. 1-13
Author(s):  
Shantanu Chakraborty ◽  
Samit Mondal ◽  
Rima Chatterjee
Keyword(s):  
Oil And Gas ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Oil And Gas Industry ◽  
Effective Porosity ◽  
Fluid Replacement ◽  
Neutron Density ◽  
Gas Industry ◽  
Sandstone Reservoirs ◽  
Shaly Sandstone

Summary Fluid-replacement modeling (FRM) is a fundamental step in rock physics scenario modeling. The results help to conduct forward modeling for prediction of seismic signatures. Further, the analysis of the results improves the accuracy of quantitative interpretation and leads to an updated reservoir characterization. While modeling for different possible reservoir pore fluid scenarios, the quality of the results largely depends on the accuracy of the FRM. Gassmann (1951)fluid-replacement modeling (GFRM) is one of the widely adopted methods across the oil and gas industry. However, the Gassmann method assumes the reservoir as clean sandstone with connected pores. This causes Gassmann fluid-replacement results to overestimate the fluid effect in shaly sandstones. This study uses neutron and density logs to correct the overestimated results in shaly sandstone reservoirs. Due to the nature of these recordings, both of these log readings have close dependencies on the presence of shale. When the logs are plotted in a justified scale, the differences between the logs provide an accurate measurement of shaliness within the reservoir. The study has formulated a weight factor using the logs, which has further been used to scale the overestimated Gassmann-modeled fluid effect. The results of the revised method are independent of type of clay presence and associated effective porosity. Moreover, the corrected FRM results from the revised Gassmann method shows good agreement with rock physical interpretation of shaly sandstone reservoirs.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Seismic reservoir characterization of a U.S. Midcontinent fluvial system using rock physics, poststack seismic attributes, and neural networks

2002 ◽  
Vol 21 (5) ◽  
pp. 428-436 ◽  
Author(s):  
Joel D. Walls ◽  
M. Turhan Taner ◽  
Gareth Taylor ◽  
Maggie Smith ◽  
Matthew Carr ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Attributes ◽  
Fluvial System ◽  
Seismic Reservoir
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