Petrophysical Rock Typing in Organic-Rich Source Rocks Using Well Logs

2013 ◽  
Author(s):  
Alvaro Aranibar ◽  
Mehrnoosh Saneifar ◽  
Zoya Heidari
Keyword(s):  
Rich Source ◽  
Source Rocks ◽  
Well Logs ◽  
Rock Typing

Rock-physics analysis of clay-rich source rocks on the Norwegian Shelf

2015 ◽  
Vol 34 (11) ◽  
pp. 1340-1348 ◽  
Author(s):  
Per Avseth ◽  
José M. Carcione
Keyword(s):  
Rock Physics ◽  
Rich Source ◽  
Source Rocks

Evolution of organo-clay composites with respect to thermal maturity in type II organic-rich source rocks

2016 ◽  
Vol 195 ◽  
pp. 68-83 ◽  
Author(s):  
Jeremie Berthonneau ◽  
Olivier Grauby ◽  
Muhannad Abuhaikal ◽  
Roland J.-M. Pellenq ◽  
Franz J. Ulm ◽  
...  
Keyword(s):  
Rich Source ◽  
Source Rocks ◽  
Type Ii ◽  
Thermal Maturity ◽  
Organo Clay

DETERMINATION FROM WELL LOGS OF THE TOTAL ORGANIC CARBON CONTENT IN POTENTIAL SOURCE ROCKS

1994 ◽  
Vol 34 (1) ◽  
pp. 307
Author(s):  
J.L. Lin ◽  
H.A. Salisch
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Source Rocks ◽  
Well Logs ◽  
Organic Carbon Content ◽  
Well Log ◽  
Single Model ◽  
Geochemical Properties ◽  
Comparison Of The Results ◽  
Immature Source Rock

This paper discusses, in some detail, the log responses to total organic carbon (TOC) in the Upper and Middle Velkerri Formation in an area of the McArthur Basin, Northern Territory, Australia. The Formation Density log was found to be superior to other standard well logs in assessing values of TOC in the area studied. A theoretical model was used to estimate TOC from the Formation Density log. The model was established and its applicability was verified by comparison with other models. Based on geochemical properties the Upper and Middle Velkerri Formation is classified into three categories: nonsource rocks, mature source rocks and immature source rock. They show significant differences in the well log responses, and different models had to be established for the three categories to determine the TOC content from well logs. Comparison of the results of using a different model for each category instead of a single model to cover the three categories shows that the former approach gives more meaningful answers.

Rock-physics templates for clay-rich source rocks

Geophysics ◽  
2015 ◽  
Vol 80 (5) ◽  
pp. D481-D500 ◽  
Author(s):  
José M. Carcione ◽  
Per Avseth
Keyword(s):  
Rock Physics ◽  
Rich Source ◽  
Source Rocks

Bayesian rock classification and petrophysical uncertainty characterization with fast well-log forward modeling in thin-bed reservoirs

2016 ◽  
Vol 4 (2) ◽  
pp. SF19-SF29
Author(s):  
Chicheng Xu ◽  
Qinshan Yang ◽  
Carlos Torres-Verdín
Keyword(s):  
Bayesian Method ◽  
Forward Modeling ◽  
Iterative Refinement ◽  
Well Logs ◽  
Petrophysical Properties ◽  
Large Uncertainty ◽  
Rock Classification ◽  
Rock Types ◽  
Rock Typing ◽  
Deterministic Methods

Rock typing is critical in deepwater reservoir characterization to construct stratigraphic models populated with static and dynamic petrophysical properties. Rock typing based on multiple well logs is subject to large uncertainty in thinly bedded reservoirs because true physical properties cannot be resolved by low-resolution logging tools due to shoulder-bed effects. We have introduced a new Bayesian approach that inherently adopts the scientific method of iterative hypothesis testing to perform rock typing by simultaneously honoring different logging-tool physics in a multilayered earth model. In addition to estimating the vertical distribution of rock types with maximum likelihood, the Bayesian method quantifies the uncertainty of rock types and the associated petrophysical properties layer by layer. Bayesian rock classification is performed with a fast sampling technique based on the Markov-chain Monte Carlo method, thereby enabling an efficient search of rock types to obtain the final results. We have used a fast linear iterative refinement method to simulate nuclear logs and a 2D forward modeling code to simulate array-induction resistivity logs. A rock-type distribution hypothesis is considered acceptable only when all the observed well logs are reproduced with forward modeling. In a field case of offshore deltaic gas reservoir, the Bayesian method differentiates rock types that exhibit subtle petrophysical variations due to grain size change. The new method provides more than 77% agreement between log- and core-derived rock types, whereas conventional deterministic methods achieve only 60% agreement due to the presence of thin beds and laminations. Even though large uncertainty is observed in thinly bedded and laminated zones, the Bayesian rock-typing method still yields rock types and petrophysical properties that agree well with core-plug measurements acquired in these layers. As a result, the overall correlation between log-derived permeability and core-measured permeability is improved by approximately 16% when compared with conventional deterministic methods.

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