Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. WA45-WA63 ◽  
Author(s):  
Dario Grana ◽  
Marco Pirrone ◽  
Tapan Mukerji
Keyword(s):  
Reservoir Characterization ◽  
Uncertainty Propagation ◽  
Rock Physics ◽  
Integrated Approach ◽  
Physical Models ◽  
Well Log ◽  
Petrophysical Properties ◽  
Formation Evaluation ◽  
Facies Classification ◽  
Well Location

Formation evaluation analysis, rock-physics models, and log-facies classification are powerful tools to link the physical properties measured at wells with petrophysical, elastic, and seismic properties. However, this link can be affected by several sources of uncertainty. We proposed a complete statistical workflow for obtaining petrophysical properties at the well location and the corresponding log-facies classification. This methodology is based on traditional formation evaluation models and cluster analysis techniques, but it introduces a full Monte Carlo approach to account for uncertainty evaluation. The workflow includes rock-physics models in log-facies classification to preserve the link between petrophysical properties, elastic properties, and facies. The use of rock-physics model predictions guarantees obtaining a consistent set of well-log data that can be used both to calibrate the usual physical models used in seismic reservoir characterization and to condition reservoir models. The final output is the set of petrophysical curves with the associated uncertainty, the profile of the facies probabilities, and the entropy, or degree of confusion, related to the most probable facies profile. The full statistical approach allows us to propagate the uncertainty from data measured at the well location to the estimated petrophysical curves and facies profiles. We applied the proposed methodology to two different well-log studies to determine its applicability, the advantages of the new integrated approach, and the value of uncertainty analysis.

scholarly journals Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study

2021 ◽  
Vol 13 (1) ◽  
pp. 1476-1493
Author(s):  
Urooj Shakir ◽  
Aamir Ali ◽  
Muhammad Raiees Amjad ◽  
Muyyassar Hussain
Keyword(s):  
Elastic Properties ◽  
Rock Physics ◽  
Integrated Approach ◽  
Poor Quality ◽  
Indus Basin ◽  
Petrophysical Properties ◽  
Gas Field ◽  
Facies Classification ◽  
Fluid Saturation ◽  
Reservoir Facies

Abstract Rock physics provides a dynamic tool for quantitative analysis by developing the basic relationship between fluid, lithological, and depositional environment of the reservoir. The elastic attributes such as impedance, density, velocity, V p/V s ratio, Mu-rho, and Lambda-rho are crucial parameters to characterize reservoir and non-reservoir facies. Rock physics modelling assists like a bridge to link the elastic properties to petrophysical properties such as porosity, facies distribution, fluid saturation, and clay/shale volume. A robust petro-elastic relationship obtained from rock physics models leads to more precise discrimination of pay and non-pay facies in the sand intervals of the study area. The Paleocene aged Lower Ranikot Formation and Pab sandstone of Cretaceous age are proven reservoirs of the Mehar gas field, Lower Indus Basin. These sands are widely distributed in the southwestern part of the basin and are enormously heterogeneous, which makes it difficult to distinguish facies and fluid content in the reservoir intervals. So, an attempt is made in this paper to separate the reservoir facies from non-reservoir facies by using an integrated approach of the petro-elastic domain in the targeted sand intervals. Furthermore, missing logs (S-sonic and P-sonic) were also synthesized in the wells and missing intervals along with improving the poor quality of the density log by captivating the washouts and other side effects. The calibrated rock physics model shows good consistency between measured and modelled logs. Petro-elastic models were predicted initially using petrophysical properties and incorporated at true reservoir conditions/parameters. Lithofacies were defined based on petrophysical cut-offs. Rock physics modelled elastic properties (Lambda-rho versus Mu-rho, impedance versus V p/V s ratio) were then cross-plotted by keeping lithofacies in the Z-axis. The cross-plots clearly separated and demarcated the litho-fluid classes (wet sand, gas sand, shale, and limestone) with specific orientation/patterns which were randomized in conventional petrophysical analysis.

An integrated seismic reservoir characterization workflow to predict hydrocarbon production capacity in unconventional plays

2013 ◽  
Vol 1 (2) ◽  
pp. SB15-SB25
Author(s):  
Gorka Garcia Leiceaga ◽  
Mark Norton ◽  
Joël Le Calvez
Keyword(s):  
Elastic Properties ◽  
Reservoir Characterization ◽  
Production Capacity ◽  
Rock Physics ◽  
Well Log ◽  
Hydrocarbon Production ◽  
Shale Formations ◽  
Data Inversion ◽  
Seismic Reservoir ◽  
Petrophysical Evaluation

Seismic-derived elastic properties may be used to help evaluate hydrocarbon production capacity in unconventional plays such as tight or shale formations. By combining prestack seismic and well log data, inversion-based volumes of elastic properties may be produced. Moreover, a petrophysical evaluation and rock physics analysis may be carried out, thus leading to a spatial distribution of hydrocarbon production capacity. The result obtained is corroborated with the available well information, confirming our ability to accurately predict hydrocarbon production capacity in unconventional plays.

Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. O21-O37 ◽  
Author(s):  
Dario Grana ◽  
Ernesto Della Rossa
Keyword(s):  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Joint Estimation ◽  
Physical Models ◽  
Inversion Method ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Statistical Rock Physics

A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and linearized seismic modeling connects velocities and density to seismic amplitude. A full Bayesian approach was adopted to propagate uncertainty from seismics to petrophysics in an integrated framework that takes into account different sources of uncertainty: heterogeneity of the real data, approximation of physical models, measurement errors, and scale changes. The method has been tested, as a feasibility step, on real well data and synthetic seismic data to show reliable propagation of the uncertainty through the three different steps and to compare two statistical approaches: parametric and nonparametric. Application to a real reservoir study (including data from two wells and partially stacked seismic volumes) has provided as a main result the probability densities of petrophysical properties and litho-fluid classes. It demonstrated the applicability of the proposed inversion method.

scholarly journals Integrated Well Data And 3D Seismic Inversion Study For Reservoir Delineation And Description

2020 ◽  
Vol 70 (1) ◽  
pp. 209-220
Author(s):  
Qazi Sohail Imran ◽  
◽  
Numair Ahmad Siddiqui ◽  
Abdul Halim Abdul Latif ◽  
Yasir Bashir ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Depositional Environments ◽  
Well Log ◽  
Reservoir Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
And Performance ◽  
Well Log Analysis

Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.

Facies — The drivers for modern inversions

2020 ◽  
Vol 39 (2) ◽  
pp. 102-109
Author(s):  
John Pendrel ◽  
Henk Schouten
Keyword(s):  
Bayesian Analysis ◽  
Prior Information ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Prior Probabilities ◽  
Physics Model ◽  
Rock Physics Model

It is common practice to make facies estimations from the outcomes of seismic inversions and their derivatives. Bayesian analysis methods are a popular approach to this. Facies are important indicators of hydrocarbon deposition and geologic processes. They are critical to geoscientists and engineers. The application of Bayes’ rule maps prior probabilities to posterior probabilities when given new evidence from observations. Per-facies elastic probability density functions (ePDFs) are constructed from elastic-log and rock-physics model crossplots, over which inversion results are superimposed. The ePDFs are templates for Bayesian analysis. In the context of reservoir characterization, the new information comes from seismic inversions. The results are volumes of the probabilities of occurrences of each of the facies at all points in 3D space. The concepts of Bayesian inference have been applied to the task of building low-frequency models for seismic inversions without well-log interpolation. Both a constant structurally compliant elastic trend approach and a facies-driven method, where models are constructed from per-facies trends and initial facies estimates, have been tested. The workflows make use of complete 3D prior information and measure and account for biases and uncertainties in the inversions and prior information. Proper accounting for these types of effects ensures that rock-physics models and inversion data prepared for reservoir property analysis are consistent. The effectiveness of these workflows has been demonstrated by using a Gulf of Mexico data set. We have shown how facies estimates can be effectively used to build reasonable low-frequency models for inversion, which obviate the need for well-log interpolation and provide full 3D variability. The results are more accurate probability-based net-pay estimates that correspond better to geology. We evaluate the workflows by using several measures including precision, confidence, and probabilistic net pay.

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.

A NEW OPTIMIZATION METHOD FOR ENHANCED FORMATION EVALUATION AND ROBUST PHYSICS-BASED AUTOMATIC ROCK CLASSIFICATION USING HIGH-RESOLUTION CT-SCAN IMAGE DATA AND CONVENTIONAL WELL LOGS

2021 ◽  
Author(s):  
Andres Gonzalez ◽  
◽  
Zoya Heidari ◽  
Olivier Lopez ◽  
◽  
...  
Keyword(s):  
Ct Scan ◽  
Cost Function ◽  
Bulk Density ◽  
Rock Physics ◽  
Well Logs ◽  
Petrophysical Properties ◽  
Rock Classification ◽  
Formation Evaluation ◽  
Rock Fabric ◽  
Porosity And Permeability

Conventional formation evaluation provides fast and accurate estimations of petrophysical properties in conventional formations through conventional well logs and routine core analysis (RCA) data. However, as the complexity of the evaluated formations increases conventional formation evaluation fails to provide accurate estimates of petrophysical properties. This inaccuracy is mainly caused by rapid variation in rock fabric (i.e., spatial distribution of rock components) not properly captured by conventional well logging tools and interpretation methods. Acquisition of high-resolution whole-core computed tomography (CT) scanning images can help to identify rock-fabric-related parameters that can enhance formation evaluation. In a recent publication, we introduced a permeability-based cost function for rock classification, optimization of the number of rock classes, and estimation of permeability. Incorporation of additional petrophysical properties into the proposed cost function can improved the reliability of the detected rock classes and ultimately improve the estimation of class-based petrophysical properties. The objectives of this paper are (a) to introduce a robust optimization method for rock classification and estimation of petrophysical properties, (b), to automatically employ whole-core two-dimensional (2D) CT-scan images and slabbed whole-core photos for enhanced estimates of petrophysical properties, (c) to integrate whole-core CT-scan images and slabbed whole-core photos with well logs and RCA data for automatic rock classification, (d) to derive class-based rock physics models for improved estimates of petrophysical properties. First, we conducted formation evaluation using well logs and RCA data for estimation of petrophysical properties. Then, we derived quantitative features from 2D CT-scan images and slabbed whole-core photos. We employed image-based features, RCA data and CT-scan-based bulk density for optimization of the number rock classes. Optimization of rock classes was accomplished using a physics-based cost function (i.e., a function of petrophysical properties of the rock) that compares class-based estimates of petrophysical properties (e.g., permeability and porosity) with core-measured properties for increasing number of image-based rock classes. The cost function is computed until convergence is achieved. Finally, we used class-based rock physics models for improved estimates of porosity and permeability. We demonstrated the reliability of the proposed method using whole-core CT-scan images and core photos from two siliciclastic depth intervals with measurable variation in rock fabric. We used well logs, RCA data, and CT-scan-based bulk-density. The advantages of using whole-core CT-scan data are two-fold. First, it provides high-resolution quantitative features that capture rapid spatial variation in rock fabric allowing accurate rock classification. Second, the use of CT-scan-based bulk density improved the accuracy of class-based porosity-bulk density models. The optimum number of rock classes was consistent for all the evaluated cost functions. Class-based rock physics models improved the estimates of porosity and permeability values. A unique contribution of the introduced workflow when compared to previously documented image-based rock classification workflows is that it simultaneously improves estimates of both porosity and permeability, and it can capture rock class that might not be identifiable using conventional rock classification techniques.

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