Net‐to‐gross from AI‐SI and AI‐GI crossplotting: a rock properties analysis using data from Horn Mountain, deep‐water Gulf of Mexico

Davis, Stan

doi:10.1190/1.1817206

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…EEI approaches acoustic impedance (AI) as χ tends to zero, and it tends to gradient impedance (GI) as χ tends to 90 o . Applications of AI and GI for AVO analysis and calibration as well as determination of net-to-gross have been discussed by Vernik and Fisher (2001) and Davis (2002).…”

Section: Identification Of Thin Sweet Spots In the Duvernay Formationmentioning

confidence: 99%

Estimation of density from seismic data without long offsets – a novel approach.

Sharma¹,

Chopra²

2015

SEG Technical Program Expanded Abstracts 2015

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Estimation of density plays an important role in characterizing subsurface reservoirs. Reliable determination of density from noise-free seismic data requires long offsets, or it can be determined from measured converted waves. As the acquisition, processing and interpretation of multicomponent seismic data entails more time and cost, their use has been slow in our industry. Considering the importance of the density attribute in the determination of lithology and fluid discrimination, we describe a novel approach for its determination from conventional (PP) seismic data. The key point of this approach is that it does not require long offsets. Though this methodology has been applied to a variety of reservoir characterization exercises, we describe its application to the Montney Shale Formation in the Montney-Dawson area of British Columbia, Canada. We also demonstrate a comparison of the proposed approach with simultaneous impedance inversion application to longoffset seismic data for determination of density. The proposed approach has shown encouraging results.

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Section: Identification Of Thin Sweet Spots In the Duvernay Formationmentioning

confidence: 99%

Estimation of density from seismic data without long offsets – a novel approach.

Sharma¹,

Chopra²

2015

SEG Technical Program Expanded Abstracts 2015

View full text Add to dashboard Cite

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Modeling Net-to-gross in Deepwater Reservoirs

Chen

Larson

2013

All Days

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Use of Net-to-Gross (NTG) concept in reservoir modeling has been a debate as it involves choice of cutoff, a simplified representation of reality. In-place hydrocarbon volumes highly depend on the cutoff, thus some authors suggest avoiding use of this concept. However, when building a reservoir model, a process involving upscaling the well log curves to the reservoir grid resolution is often needed. This requires the investigators to account for the portion of an active cell that can allow fluid to flow. Thus, when carefully evaluated, the NTG method can be a necessary and acceptable approximation. From outcrop studies we also observe that no turbidite sequences have consisted of 100% net reservoir sands. The variations of net sand percentages depend on the vertical and lateral variations of strata that serve as the building bricks of the overall sequences. Several methods have been tested to model the NTG for various deepwater reservoirs in the Gulf of Mexico (GOM):  Calibration of acoustic impedance (AI) curve into NTG from each individual well and propagating the NTG using stochastic method guided by seismically inverted AI and facies distribution  Scaling Gamma Ray (GR) or shale volume (VSH) curve to NTG by fixing the end members (sand and shale), scaling the curves linearly, and then conducting Sequential Gaussian Simulation to propagate NTG from wells to the reservoir model  Using VSH cutoff to generate initial NTG for the wells, correlating the NTG with AI, and developing multi-relationships from the NTG-AI space to estimate NTG at the model scale. The multi-relationships serve as a tool for sensitivity test in the reservoir model  Using total porosity frequency distribution from well logs to separate effective porosity for reservoir sands and uneffective porosity for non-reservoir rocks, then using porosity cutoff to distinguish sand from shale, and upscale the well log curves to model grid resolution to account for NTG  Estimating NTG directly from whole cores Using some case studies, we compared the difference of in-place volumes estimated based on different approaches. The studies show that as long as the assumptions for each method are physically sound, the NTG estimates are consistent. We conclude that the NTG of deepwater reservoir sands and their connectivity are the most important factors controlling total resource distribution and flow capability.

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Net‐to‐gross from AI‐SI and AI‐GI crossplotting: a rock properties analysis using data from Horn Mountain, deep‐water Gulf of Mexico

Cited by 2 publications

References 2 publications

Estimation of density from seismic data without long offsets – a novel approach.

Estimation of density from seismic data without long offsets – a novel approach.

Modeling Net-to-gross in Deepwater Reservoirs

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