scholarly journals Digital carbonate rock physics

Solid Earth ◽  
2016 ◽  
Vol 7 (4) ◽  
pp. 1185-1197 ◽  
Author(s):  
Erik H. Saenger ◽  
Stephanie Vialle ◽  
Maxim Lebedev ◽  
David Uribe ◽  
Maria Osorno ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Carbonate Rock ◽  
Rock Physics ◽  
Rock Properties ◽  
Micro Computed Tomography ◽  
Low Resolution ◽  
Two Phase ◽  
Data Set ◽  
Rock Characterization ◽  
Low Resolution Images

Abstract. Modern estimation of rock properties combines imaging with advanced numerical simulations, an approach known as digital rock physics (DRP). In this paper we suggest a specific segmentation procedure of X-ray micro-computed tomography data with two different resolutions in the µm range for two sets of carbonate rock samples. These carbonates were already characterized in detail in a previous laboratory study which we complement with nanoindentation experiments (for local elastic properties). In a first step a non-local mean filter is applied to the raw image data. We then apply different thresholds to identify pores and solid phases. Because of a non-neglectable amount of unresolved microporosity (micritic phase) we also define intermediate threshold values for distinct phases. Based on this segmentation we determine porosity-dependent values for effective P- and S-wave velocities as well as for the intrinsic permeability. For effective velocities we confirm an observed two-phase trend reported in another study using a different carbonate data set. As an upscaling approach we use this two-phase trend as an effective medium approach to estimate the porosity-dependent elastic properties of the micritic phase for the low-resolution images. The porosity measured in the laboratory is then used to predict the effective rock properties from the observed trends for a comparison with experimental data. The two-phase trend can be regarded as an upper bound for elastic properties; the use of the two-phase trend for low-resolution images led to a good estimate for a lower bound of effective elastic properties. Anisotropy is observed for some of the considered subvolumes, but seems to be insignificant for the analysed rocks at the DRP scale. Because of the complexity of carbonates we suggest using DRP as a complementary tool for rock characterization in addition to classical experimental methods.

scholarly journals Digital Carbonate Rock Physics

2016 ◽  
Author(s):  
Erik H. Saenger ◽  
Stephanie Vialle ◽  
Maxim Lebedev ◽  
David Uribe ◽  
Maria Osorno ◽  
...  
Keyword(s):  
Carbonate Rock ◽  
Image Data ◽  
Rock Physics ◽  
Rock Properties ◽  
Micro Computed Tomography ◽  
S Wave ◽  
Digital Rock Physics ◽  
Intermediate Phases ◽  
Mean Filter ◽  
Rock Characterization

Abstract. Modern estimation of rock properties combines imaging with advanced numerical simulations, an approach known as Digital Rock Physics (DRP). In this paper we suggest a specific segmentation procedure of X-Ray micro-Computed Tomography data with two different resolutions for two sets of carbonate rock samples. These carbonates were already characterized in detail in a previous laboratory study which we complement with nano-indentation experiments. In a first step a non-local mean filter is applied to the raw image data. We then apply different thresholds to identify pores and solid phases. Because of a non-neglectable amount of unresolved micro-porosity (“micritic phase”) we also define intermediate phases. Based on this segmentation we determine porosity-dependent values for P- and S-wave velocities as well as for the intrinsic permeability. The porosity measured in the laboratory is then used to predict the effective rock properties for a comparison with experimental data. Anisotropy is observed for some sub-samples, but seems to be insignificant in our case. Because of the complexity of carbonates we suggest to use DRP as a complementary tool for rock characterization in addition to classical experimental methods.

Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Djamel Ouzzane ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Carbonate Rock ◽  
Chemical Properties ◽  
Rock Physics ◽  
Semantic Segmentation ◽  
Rock Properties ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Rock Types ◽  
Fluid Saturation ◽  
Rock Typing

<p><a></a>Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture.<br></p>

scholarly journals Rock Physics Modeling and Seismic Interpretation to Estimate Shally Cemented Zone in Carbonate Reservoir Rock

2016 ◽  
Vol 1 (1) ◽  
pp. 45
Author(s):  
Handoyo Handoyo ◽  
M Rizki Sudarsana ◽  
Restu Almiati
Keyword(s):  
Elastic Properties ◽  
Wave Velocity ◽  
Carbonate Rock ◽  
Rock Physics ◽  
Carbonate Reservoir ◽  
Reservoir Rock ◽  
Rock Physics Modeling ◽  
Porosity And Permeability ◽  
Physics Modeling ◽  
The Relationship

Carbonate rock are important hydrocarbon reservoir rocks with complex texture and petrophysical properties (porosity and permeability). These complexities make the prediction reservoir characteristics (e.g. porosity and permeability) from their seismic properties more difficult. The goal of this paper are to understanding the relationship of physical properties and to see the signature carbonate initial rock and shally-carbonate rock from the reservoir. To understand the relationship between the seismic, petrophysical and geological properties, we used rock physics modeling from ultrasonic P- and S- wave velocity that measured from log data. The measurements obtained from carbonate reservoir field (gas production). X-ray diffraction and scanning electron microscope studies shown the reservoir rock are contain wackestone-packstone content. Effective medium theory to rock physics modeling are using Voigt, Reuss, and Hill.  It is shown the elastic moduly proposionally decrease with increasing porosity. Elastic properties and wave velocity are decreasing proporsionally with increasing porosity and shally cemented on the carbonate rock give higher elastic properties than initial carbonate non-cemented. Rock physics modeling can separated zones which rich of shale and less of shale.

Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Djamel Ouzzane ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Carbonate Rock ◽  
Chemical Properties ◽  
Rock Physics ◽  
Semantic Segmentation ◽  
Rock Properties ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Rock Types ◽  
Fluid Saturation ◽  
Rock Typing

<p><a></a>Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture.<br></p>

Reservoir properties prediction integrating controlled-source electromagnetic, prestack seismic, and well-log data using a rock-physics framework: Case study in the Hoop Area, Barents Sea, Norway

2017 ◽  
Vol 5 (2) ◽  
pp. SE43-SE60 ◽  
Author(s):  
Pedro Alvarez ◽  
Amanda Alvarez ◽  
Lucy MacGregor ◽  
Francisco Bolivar ◽  
Robert Keirstead ◽  
...  
Keyword(s):  
Seismic Data ◽  
Barents Sea ◽  
Rock Physics ◽  
Positive Outcome ◽  
Rock Properties ◽  
Integrated Analysis ◽  
Reservoir Properties ◽  
Data Set ◽  
Controlled Source ◽  
Rotation Scheme

We have developed an example from the Hoop Area of the Barents Sea showing a sequential quantitative integration approach to integrate seismic and controlled-source electromagnetic (CSEM) attributes using a rock-physics framework. The example illustrates a workflow to address the challenges of multiphysics and multiscale data integration for reservoir characterization purposes. A data set consisting of 2D GeoStreamer seismic and towed streamer electromagnetic data that were acquired concurrently in 2015 by PGS provide the surface geophysical measurements that we used. Two wells in the area — Wisting Central (7324/8-1) and Wisting Alternative (7324/7-1S) — provide calibration for the rock-physics modeling and the quantitative integrated analysis. In the first stage of the analysis, we invert prestack seismic and CSEM data separately for impedance and anisotropic resistivity, respectively. We then apply the multi-attribute rotation scheme (MARS) to estimate rock properties from seismic data. This analysis verified that the seismic data alone cannot distinguish between commercial and noncommercial hydrocarbon saturation. Therefore, in the final stage of the analysis, we invert the seismic and CSEM-derived properties within a rock-physics framework. The inclusion of the CSEM-derived resistivity information within the inversion approach allows for the separation of these two possible scenarios. Results reveal excellent correlation with known well outcomes. The integration of seismic, CSEM, and well data predicts very high hydrocarbon saturations at Wisting Central and no significant saturation at Wisting Alternative, consistent with the findings of each well. Two further wells were drilled in the area and used as blind tests in this case: The slightly lower saturation predicted at Hanssen (7324/7-2) is related to 3D effects in the CSEM data, but the positive outcome of the well is correctly predicted. At Bjaaland (7324/8-2), although the seismic indications are good, the integrated interpretation result predicts correctly that this well was unsuccessful.

Stuck between a rock and a reflection: A tutorial on low-frequency models for seismic inversion

2017 ◽  
Vol 5 (2) ◽  
pp. B17-B27 ◽  
Author(s):  
Mark Sams ◽  
David Carter
Keyword(s):  
Elastic Properties ◽  
Model Building ◽  
Seismic Velocity ◽  
Low Frequency ◽  
Rock Physics ◽  
Frequency Component ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Seismic Velocities ◽  
Frequency Model

Predicting the low-frequency component to be used for seismic inversion to absolute elastic rock properties is often problematic. The most common technique is to interpolate well data within a structural framework. This workflow is very often not appropriate because it is too dependent on the number and distribution of wells and the interpolation algorithm chosen. The inclusion of seismic velocity information can reduce prediction error, but it more often introduces additional uncertainties because seismic velocities are often unreliable and require conditioning, calibration to wells, and conversion to S-velocity and density. Alternative techniques exist that rely on the information from within the seismic bandwidth to predict the variations below the seismic bandwidth; for example, using an interpretation of relative properties to update the low-frequency model. Such methods can provide improved predictions, especially when constrained by a conceptual geologic model and known rock-physics relationships, but they clearly have limitations. On the other hand, interpretation of relative elastic properties can be equally challenging and therefore interpreters may find themselves stuck — unsure how to interpret relative properties and seemingly unable to construct a useful low-frequency model. There is no immediate solution to this dilemma; however, it is clear that low-frequency models should not be a fixed input to seismic inversion, but low-frequency model building should be considered as a means to interpret relative elastic properties from inversion.

Missing well log prediction using convolutional long short-term memory network

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA159-WA171 ◽  
Author(s):  
Nam Pham ◽  
Xinming Wu ◽  
Ehsan Zabihi Naeini
Keyword(s):  
Continental Shelf ◽  
Gamma Ray ◽  
Short Term Memory ◽  
Rock Physics ◽  
Rock Properties ◽  
Well Log ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

Reservoir characterization involves integration of different types of data to understand the subsurface rock properties. To incorporate multiple well log types into reservoir studies, estimating missing logs is an essential step. We have developed a method to estimate missing well logs by using a bidirectional convolutional long short-term memory (bidirectional ConvLSTM) cascaded with fully connected neural networks. We train the model on 177 wells from mature areas of the UK continental shelf (UKCS). We test the trained model on one blind well from UKCS, three wells from the Volve field in the Norwegian continental shelf, one well from the Penobscot field in the Scotian shelf offshore Canada, and one well from the Teapot Dome data set in Wyoming. The method takes into account the depth trend and the local shape of logs by using ConvLSTM architecture. The method is examined on sonic log prediction and can produce an accurate prediction of sonic logs from gamma-ray, density, and neutron porosity logs. The advantages of our method are that it is not applied on an interval by interval basis like rock-physics-based methods and it also outputs the uncertainties facilitated by dropout layers and Monte Carlo sampling at inference time.

Investigating the influence of mineralogy and pore shape on the velocity of carbonate rocks: Insights from extant global data sets

2015 ◽  
Vol 3 (1) ◽  
pp. SA15-SA31 ◽  
Author(s):  
Mark G. Kittridge
Keyword(s):  
Elastic Properties ◽  
Rock Physics ◽  
Theoretical Models ◽  
P Wave ◽  
Data Sets ◽  
Pore Shape ◽  
Data Set ◽  
S Waves ◽  
Extreme Variability ◽  
Global Data

Using a variety of recent public-domain data sets comprising porosity, velocity (P- and S-waves), and, in most cases, mineralogy and petrographic data, I created an extensive global data set and evaluated the importance of mineralogy and pore type on the elastic properties behavior of carbonate core plugs. Results from this investigation clearly illuminated the potential for overinterpreting elastic properties behavior as a function of pore type(s) when mineralogy was not explicitly included in the analysis. Rock-physics analysis using a combination of heuristic and theoretical models illustrated that mineralogy exerted a significant additional variation on velocity at a given porosity. Failure to account for mineralogy exacerbated inferences about the effect of pore type(s) made using a comparison of P-wave velocity to an inappropriate empirical model (Wyllie) that did not account for pore shape(s). In this analysis, extreme variability in carbonate velocity was observed in only portions of two data sets, when mineralogy was explicitly considered and robust models that accounted for inclusion (pore) shape were used. Results from this analysis resulted in a recommended workflow, including a rock-physics template and dry-rock modulus diagnostics, for the evaluation of lab-based carbonate rock-physics data. The workflow was amenable to further integration with well-based data and other core-based petrophysical measurements (e.g., electrical properties).

Predicting elastic properties and permeability of rocks from 2D thin sections

2018 ◽  
Vol 37 (6) ◽  
pp. 421-427 ◽  
Author(s):  
Nattavadee Srisutthiyakorn ◽  
Sander Hunter ◽  
Rituparna Sarker ◽  
Ronny Hofmann ◽  
Irene Espejo
Keyword(s):  
High Resolution ◽  
Elastic Properties ◽  
Rock Physics ◽  
Depositional Environments ◽  
Rock Properties ◽  
Thin Sections ◽  
2D Simulation ◽  
Computed Tomography Images ◽  
The Difference ◽  
Third Dimension

Predicting rock elastic properties and permeability from high-resolution 2D thin sections has been a challenging problem in rock physics because the 2D thin sections reveal very little about how the microstructure connects in the third dimension. However, 2D thin sections are widely available and inexpensive because they are often produced as a part of the reservoir-quality workflow. Furthermore, they have much higher resolution and greater field of view than micro X-ray computed tomography images, which are commonly used for rock properties estimation. The 2D thin sections we studied are from various hydrocarbon-bearing clastic formations with a variety of provenances, depositional environments, and burial histories. The high-resolution 2D images were scanned from these physical 2D thin sections. K-means segmentation was then employed to identify different minerals and pores for creating 2D binary images. The focus of this study is to simulate 2D elastic properties and permeability from 2D thin sections and then to employ various empirical relations to transform these 2D simulation results to 3D intrinsic rock properties. We compared the rock properties from this process to those from core measurements and measured wireline logs and found that these 2D to 3D rock property transformations yield promising results, especially for elastic properties. The results show that 2D thin section images have high enough resolution to resolve grain contacts very well. Predicting the permeability from 2D thin sections is still challenging since the process requires fitting the physical equation in order to retrieve the fitting coefficient for prediction due to our lack of understanding of the difference between 2D and 3D pore size distribution.

Testing popular rock-physics models

2019 ◽  
Vol 38 (5) ◽  
pp. 350-357 ◽  
Author(s):  
Alan Mur ◽  
Lev Vernik
Keyword(s):  
Effective Stress ◽  
Rock Physics ◽  
Rock Properties ◽  
Quantitative Interpretation ◽  
Data Set ◽  
Rock Property ◽  
Dynamic Impedance ◽  
The World ◽  
Elastic Rock ◽  
Rock Parameters

In the spirit of classic rock physics, and as an ideal foundation for conventional quantitative interpretation workflows, we consider several popular models relating elastic rock properties to their composition, microstructure, and effective stress on the background of a worldwide log data set, incorporating sands and shales characterized by the maximum dynamic impedance range. We demonstrate that the patchy cement model, ellipsoidal inclusion model, and siliciclastic diagenesis model may be calibrated successfully against the world data set and used in seismic rock property log restoration/editing. We also demonstrate that some of these models present obvious challenges in terms of the information derived from quantitative seismic interpretation. Notably, the key input parameters used in these rock-physics models may show little resemblance to the rock parameters actually observed in geologic studies. Replacing the true rock parameters with the effective ones may do disservice to the science of rock physics in general.

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