scholarly journals Rock physics diagnostic of Eocene Sokor-1 reservoir in Termit subbasin, Niger

2021 ◽  
Vol 11 (9) ◽  
pp. 3361-3371
Author(s):  
Amadou Hassane ◽  
Chukwuemeka Ngozi Ehirim ◽  
Tamunonengiyeofori Dagogo
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Low Contrast ◽  
Unconsolidated Sands ◽  
High Impedance ◽  
Impedance Characteristics ◽  
The Matrix ◽  
Fluid Properties ◽  
Resistivity Log ◽  
Diagnostic Modeling

AbstractEocene Sokor-1 reservoir is intrinsically heterogeneous and characterized by low-contrast low-resistivity log responses in parts of the Termit subbasin. Discriminating lithology and fluid properties using petrophysics alone is complicated and undermines reservoir characterization. Petrophysics and rock physics were integrated through rock physics diagnostics (RPDs) modeling for detailed description of the reservoir microstructure and quality in the subbasin. Petrophysical evaluation shows that Sokor-1 sand_5 interval has good petrophysical properties across wells and prolific in hydrocarbons. RPD analysis revealed that this sand interval could be best described by the constant cement sand model in wells_2, _3, _5 and _9 and friable sand model in well_4. The matrix structure varied mostly from clean and well-sorted unconsolidated sands as well as consolidated and cemented sandstones to deteriorating and poorly sorted shaly sands and shales/mudstones. The rock physics template built based on the constant cement sand model for representative well_2 diagnosed hydrocarbon bearing sands with low Vp/Vs and medium-to-high impedance signatures. Brine shaly sands and shales/mudstones were diagnosed with moderate Vp/Vs and medium-to-high impedance and high Vp/Vs and medium impedance, respectively. These results reveal that hydrocarbon sands and brine shaly sands cannot be distinctively discriminated by the impedance property, since they exhibit similar impedance characteristics. However, hydrocarbon sands, brine shaly sands and shales/mudstones were completely discriminated by characteristic Vp/Vs property. These results demonstrate the robust application of rock physics diagnostic modeling in quantitative reservoir characterization and may be quite useful in undrilled locations in the subbasin and fields with similar geologic settings.

scholarly journals Correlations of Rock-Physics Model Parameters From Bayesian Analysis: Pressure- and Porosity-Dependent Models Applied to Unconsolidated Sands

2022 ◽  
Vol 9 ◽  
Author(s):  
Kyle T. Spikes ◽  
Mrinal K. Sen
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Model Parameters ◽  
Data Sets ◽  
Unconsolidated Sands ◽  
Bayesian Formulation ◽  
Input Parameters ◽  
Physics Model ◽  
Rock Physics Model ◽  
Prior Models

Correlations of rock-physics model inputs are important to know to help design informative prior models within integrated reservoir-characterization workflows. A Bayesian framework is optimal to determine such correlations. Within that framework, we use velocity and porosity measurements on unconsolidated, dry, and clean sands. Three pressure- and three porosity-dependent rock-physics models are applied to the data to examine relationships among the inputs. As with any Bayesian formulation, we define a prior model and calculate the likelihood in order to evaluate the posterior. With relatively few inputs to consider for each rock-physics model, we found that sampling the posterior exhaustively to be convenient. The results of the Bayesian analyses are multivariate histograms that indicate most likely values of the input parameters given the data to which the rock-physics model was fit. When the Bayesian procedure is repeated many times for the same data, but with different prior models, correlations emerged among the input parameters in a rock-physics model. These correlations were not known previously. Implications, for the pressure- and porosity-dependent models examined here, are that these correlations should be utilized when applying these models to other relevant data sets. Furthermore, additional rock-physics models should be examined similarly to determine any potential correlations in their inputs. These correlations can then be taken advantage of in forward and inverse problems posed in reservoir characterization.

Distinguishing gas-bearing sandstone reservoirs within mixed siliciclastic-carbonate sequences using extended elastic impedance: Nile Delta — Egypt

2016 ◽  
Vol 4 (4) ◽  
pp. T427-T441 ◽  
Author(s):  
Ahmed Hafez ◽  
John P. Castagna
Keyword(s):  
Nile Delta ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Anomalous Behavior ◽  
Well Logs ◽  
High Impedance ◽  
Bright Spots ◽  
Fluid Properties ◽  
Elastic Impedance ◽  
Gas Bearing

In the Abu Madi Formation of the Nile Delta Basin, false bright spots may be misinterpreted as being indicative of hydrocarbons due to mixed clastics and carbonates. However, rock-physics analysis of well logs in a particular prospect area where such ambiguity exists suggests that attributes derived using extended elastic impedance (EEI) inversion may help identify hydrocarbons because they better show anomalous behavior in particular directions that are readily related to pore fluids and lithology. The EEI attributes calculated from well logs correlate extremely well to lithology and fluid properties, thereby differentiating amplitude anomalies caused by gas-bearing sandstones encased in shale from similar amplitudes caused by juxtaposition of high-impedance carbonates over lower impedance water-filled sandstones. Comparing seismically derived EEI attributes to well logs from a productive well and a nonproductive well indicates that seismic inversion can successfully identify lithologies such as shales, sandstones, carbonates, and anhydrite and distinguish gas-bearing from water-bearing sandstones. The technique can thus potentially be used to better delineate and risk prospects in the area, as well as assisting exploration efforts in other locations where similar ambiguities in amplitude interpretation exist.

scholarly journals Stochastic reservoir characterization using prestack seismic data

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 978-993 ◽  
Author(s):  
Jo Eidsvik ◽  
Per Avseth ◽  
Henning Omre ◽  
Tapan Mukerji ◽  
Gary Mavko
Keyword(s):  
Reservoir Characterization ◽  
A Priori ◽  
Rock Physics ◽  
Real Data ◽  
Spatial Coupling ◽  
The North ◽  
Amplitude Versus Offset ◽  
Likelihood Model ◽  
Avo Attributes ◽  
The Impact

Reservoir characterization must be based on information from various sources. Well observations, seismic reflection times, and seismic amplitude versus offset (AVO) attributes are integrated in this study to predict the distribution of the reservoir variables, i.e., facies and fluid filling. The prediction problem is cast in a Bayesian setting. The a priori model includes spatial coupling through Markov random field assumptions and intervariable dependencies through nonlinear relations based on rock physics theory, including Gassmann's relation. The likelihood model relating observations to reservoir variables (including lithology facies and pore fluids) is based on approximations to Zoeppritz equations. The model assumptions are summarized in a Bayesian network illustrating the dependencies between the reservoir variables. The posterior model for the reservoir variables conditioned on the available observations is defined by the a priori and likelihood models. This posterior model is not analytically tractable but can be explored by Markov chain Monte Carlo (MCMC) sampling. Realizations of reservoir variables from the posterior model are used to predict the facies and fluid‐filling distribution in the reservoir. A maximum a posteriori (MAP) criterion is used in this study to predict facies and pore‐fluid distributions. The realizations are also used to present probability maps for the favorable (sand, oil) occurrence in the reservoir. Finally, the impact of seismic AVO attributes—AVO gradient, in particular—is studied. The approach is demonstrated on real data from a turbidite sedimentary system in the North Sea. AVO attributes on the interface between reservoir and cap rock are extracted from 3D seismic AVO data. The AVO gradient is shown to be valuable in reducing the ambiguity between facies and fluids in the prediction.

Dynamic to Static Modeling

Geophysics ◽  
2021 ◽  
pp. 1-43
Author(s):  
Javad Sharifi
Keyword(s):  
Reservoir Characterization ◽  
Triaxial Compression ◽  
Rock Physics ◽  
Regression Equations ◽  
Compression Tests ◽  
In Situ Tests ◽  
Factors Affecting ◽  
Static Modulus ◽  
Wide Range ◽  
Static Modeling

Dynamic-to-static modulus conversion has long been recognized as a complicated and challenging task in reservoir characterization and seismic geomechanics, and many single- and two-variable regression equations have been proposed. In practice however, the form and constants of the regression equation are variable from case to case. I introduce a methodology for estimating the static moduli called dynamic-to-static modeling (DTS). The methodology was validated by laboratory tests (ultrasonic and triaxial compression tests) to obtain dynamic and quasi-static bulk and Young’s (elasticity) moduli. Next, rock deformation phenomena were simulated considering different parameters affecting the process. The dynamic behavior was further modeled using rock physics methods. Unlike the conventional dynamic-to-static conversion procedures, the method considers a wide range of factors affecting the relationship between the dynamic and static moduli, including strain amplitude, dispersion, rock failure mechanism, pore shape, crack parameters, poromechanics, and upscaling. A comparison between the data from laboratory and in-situ tests and the estimation results indicated promising findings. The accuracy of the results was assessed by the analysis of variance (ANOVA). In addition to modeling the static moduli, DTS can be used to verify the static and dynamic moduli values with appropriate accuracy when core data is not available.

A Cognitive Data-Driven Single-Well Modeling Workflow for Reservoir Deliverability Predictions – Expanding the Wireline Formation Tester Application Envelope

2021 ◽  
Author(s):  
Abdul Bari ◽  
Mohammad Rasheed Khan ◽  
M. Sohaib Tanveer ◽  
Muhammad Hammad ◽  
Asad Mumtaz Adhami ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Production Performance ◽  
Data Driven ◽  
Analytical Models ◽  
Production Model ◽  
Reservoir Evaluation ◽  
Fluid Properties ◽  
Petrophysical Model ◽  
Single Well ◽  
Data Driven Approach

Abstract In today's dynamically challenging E&P industry, exploration activities demand for out-of-the-box measures to make the most out of the data available at hand. Instead of relying on time consuming and cost-intensive deliverability testing, there is a strong push to extract maximum possible information from time- and cost-efficient wireline formation testers in combination with other openhole logs to get critical reservoir insight. Consequently, driving efficiency in the appraisal process by reducing redundant expenditures linked with reservoir evaluation. Employing a data-driven approach, this paper addresses the need to build single-well analytical models that combines knowledge of core data, petrophysical evaluation and reservoir fluid properties. Resultantly, predictive analysis using cognitive processes to determine multilayer productivity for an exploratory well is achieved. Single Well Predictive Modeling (SWPM) workflow is developed for this case which utilizes plethora of formation evaluation information which traditionally resides across siloed disciplines. A tailor-made workflow has been implemented which goes beyond the conventional formation tester deliverables while incorporating PVT and numerical simulation methodologies. Stage one involved reservoir characterization utilizing Interval Pressure Transient Testing (IPTT) done through the mini-DST operation on wireline formation tester. Stage two concerns the use of analytical modeling to yield exact solution to an approximate problem whose end-product is an estimate of the Absolute Open Flow Potential (AOFP). Stage three involves utilizing fluid properties from downhole fluid samples and integrating with core, OH logs, and IPTT answer products to yield a calibrated SWPM model, which includes development of a 1D petrophysical model. Additionally, this stage produces a 3D simulation model to yield a reservoir production performance deliverable which considers variable rock typing through neural network analysis. Ultimately, stage four combines the preceding analysis to develop a wellbore production model which aids in optimizing completion strategies. The application of this data-driven and cognitive technique has helped the operator in evaluating the potential of the reservoir early-on to aid in the decision-making process for further investments. An exhaustive workflow is in place that can be adopted for informed reservoir deliverability modeling in case of early well-life evaluations.

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