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.

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 An integrated approach for reservoir characterisation in deep-water Krishna-Godavari basin, India: a case study

2021 ◽  
Vol 18 (1) ◽  
pp. 134-144
Author(s):  
Samit Mondal ◽  
Rima Chatterjee ◽  
Shantanu Chakraborty
Keyword(s):  
Rock Physics ◽  
Integrated Approach ◽  
Reservoir Quality ◽  
Physical Analysis ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Hydrocarbon Reservoir ◽  
Fluid Saturation ◽  
Godavari Basin

Abstract The Miocene reservoirs in prolific Krishna-Godavari basin are mostly fluvial deposits and laminated or blocky in nature. The type of reservoir quality depends on associated geological environments. Due to several lateral variations in reservoir properties, a similar kind of workflow for reservoir characterisation does not work. Customised workflow needs to be applied in this area for estimation of petrophysical properties or rock physical analysis for reservoir quality prediction. As the major input of rock physical analysis is petrophysical properties, it is crucial to estimate these properties accurately. Meanwhile, it is also important to check the seismic sensitivity to change in fluid saturation in the reservoir characterisation process. The analysis assures the presence of reservoir and hydrocarbon contact in seismic sensitivity, which is essential for removing risk. Integrating the geological model with rock physical analysis for reservoir characterisation at the drilled well, the reservoir quality at undrilled prospects is predicted. In this study, the comprehensive study for reservoir characterisation of Miocene reservoirs consists of three different steps: calculation of petrophysical properties for mixed of thick and laminated sequence, rock physical analysis for identification of hydrocarbon reservoir and corresponding seismic sensitivity for change in saturation and finally the rock physics template for prediction of reservoir quality away from the drilled well. Results from the study have added significant value in de-risking the number of undrilled prospects in this area.

scholarly journals Gas-bearing sands appraisal for Zamzama Gas field in Pakistan through inverted elastic attributes assisted with PNN approximation of petrophysical properties

2021 ◽  
Author(s):  
Zahid U. Khan ◽  
◽  
Mona Lisa ◽  
Muyyassar Hussain ◽  
Syed A. Ahmed ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Clay Content ◽  
Seismic Inversion ◽  
Indus Basin ◽  
Minimal Risk ◽  
Gas Field ◽  
Lower Indus Basin ◽  
Heterogeneity Effects ◽  
Prestack Seismic Inversion ◽  
Gas Bearing

The Pab Formation of Zamzama block, lying in the Lower Indus Basin of Pakistan, is a prominent gas-producing sand reservoir. The optimized production is limited by water encroachment in producing wells, thus it is required to distinguish the gas-sand facies from the remainder of the wet sands and shales for additional drilling zones. An approach is adopted based on a relation between petrophysical and elastic properties to characterize the prospect locations. Petro-elastic models for the identified facies are generated to discriminate lithologies in their elastic ranges. Several elastic properties, including p-impedance (11,600-12,100 m/s*g/cc), s-impedance (7,000-7,330 m/s*g/cc), and Vp/Vs ratio (1.57-1.62), are calculated from the simultaneous prestack seismic inversion, allowing the identification of gas sands in the field. Furthermore, inverted elastic attributes and well-based lithologies are incorporated into the Bayesian framework to evaluate the probability of gas sands. To better determine reservoir quality, bulk volumes of PHIE and clay are estimated using elastic volumes trained on well logs employing Probabilistic Neural Networking (PNN), which effectively handles heterogeneity effects. The results showed that the channelized gas-sands passing through existing well locations exhibited reduced clay content and maximum effective porosities of 9%, confirming the reservoir's good quality. Such approaches can be widely implemented in producing fields to completely assess litho-facies and achieve maximum production with minimal risk.

Time-lapse seismic monitoring methodologies applied to the Pyrenees Field, offshore Western Australia

2015 ◽  
Vol 55 (2) ◽  
pp. 412 ◽  
Author(s):  
Ramses Meza ◽  
Guy Duncan ◽  
Konstantinos Kostas ◽  
Stanislav Kuzmin ◽  
Mauricio Florez ◽  
...  
Keyword(s):  
Seismic Response ◽  
Coming Out ◽  
Rock Physics ◽  
Time Lapse ◽  
Reservoir Rock ◽  
Gas Field ◽  
Field Development ◽  
Fluid Saturation ◽  
Band Limited ◽  
4D Seismic

Time-lapse dedicated 3D seismic surveys were acquired across the Pyrenees oil and gas field, Exmouth Sub-basin to map production-induced changes in the reservoir. Rock-physics 4D modelling showed that changes in pore pressure and fluid saturation would produce a time-lapse seismic response of sufficient magnitude, in both amplitude and velocity, to overcome time-lapse noise. The dominant observed effect is associated with gas coming out of solution. The reservoir simulation model forecasted that reservoir depletion would cause gas breakout that would impact the elastic properties of the reservoir. The effect of gas breakout can be clearly observed on the 4D seismic data as a change in both amplitude and velocity. The analysis of the seismic datasets was proven to be enhanced significantly by using inversion methodologies. These included a band-limited extended-elastic impedance (EEI) approach, as well as simultaneous 4D elastic inversion. These datasets, combined with rock physics modelling, enabled quantitative interpretation of the change in 4D seismic response which was a key tool for assisting with the infill well placement and field development strategy.

scholarly journals An integrated approach to evaluate the hydrocarbon potential of Jurassic Samana Suk Formation in Middle Indus Basin, Pakistan.

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Muhammad Armaghan F. Miraj ◽  
◽  
Abid Ali ◽  
Hassan Javaid ◽  
Pal Washa S. Rathore ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Rock Physics ◽  
Integrated Approach ◽  
Structural Features ◽  
Indus Basin ◽  
Hydrocarbon Potential ◽  
Reservoir Properties ◽  
Contour Maps ◽  
Seismic Lines ◽  
Hydrocarbon Reserves

The Indus Basin is considered as prolific hydrocarbon-bearing province of Pakistan. The study area is located in the Middle Indus Basin. Two wells (Bagh-X-01 and Budhuana-01) were drilled in the vicinity of the study area to determine the hydrocarbon potential of the area. Both wells show no hydrocarbon reserves and are thus abandoned. The present study emphasizes two-dimensional subsurface seismic interpretation and rock physics evaluation to estimate reservoir properties of the Jurassic Samana Suk Formation. Data from nine 2-D seismic lines and two wells have been utilized to evaluate the potential. The time contour maps indicate the existence of subsurface structural features in the study area. With the help of the 3-D geological model, the faults are marked in the Samana Suk Formation and the structure is identified as a monocline. The 3-D geological modeling results also reveal that Samana Suk Formation tends to become thin in the northeast, and thick in the southwest. The petrophysical interpretation was performed to find the hydrocarbon potential of the Formation. The cross plot between P-impedance and Vp/Vs ratio shows that the lithology cannot be differentiated by the logs. Rock physics parameters such as Poisson’s ratio, bulk modulus, shear modulus, shear wave velocity, primary wave velocity, Vp/Vs ratio, and density indicate that there are no considerable hydrocarbon reserves in the Samana Suk Formation.

Rock-physics transforms and scale of investigation

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. MR75-MR88 ◽  
Author(s):  
Jack Dvorkin ◽  
Uri Wollner
Keyword(s):  
Elastic Properties ◽  
Rock Physics ◽  
Clay Content ◽  
Pore Fluid ◽  
Petrophysical Properties ◽  
Reflection Seismology ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model ◽  
Seismic Scale

Rock-physics “velocity-porosity” transforms are usually established on sets of laboratory and/or well data with the latter data source being dominant in recent practice. The purpose of establishing such transforms is to (1) conduct forward modeling of the seismic response for various geologically plausible “what if” scenarios in the subsurface and (2) interpret seismic data for petrophysical properties and conditions, such as porosity, clay content, and pore fluid. Because the scale of investigation in the well is considerably smaller than that in reflection seismology, an important question is whether the rock-physics model established in the well can be used at the seismic scale. We use synthetic examples and well data to show that a rock-physics model established at the well approximately holds at the seismic scale, suggest a reason for this scale independence, and explore where it may be violated. The same question can be addressed as an inverse problem: Assume that we have a rock-physics transform and know that it works at the scale of investigation at which the elastic properties are seismically measured. What are the upscaled (smeared) petrophysical properties and conditions that these elastic properties point to? It appears that they are approximately the arithmetically volume-averaged porosity and clay content (in a simple quartz/clay setting) and are close to the arithmetically volume-averaged bulk modulus of the pore fluid (rather than averaged saturation).

Multivariate probabilistic rock physics models using Kumaraswamy distributions

Geophysics ◽  
2021 ◽  
pp. 1-43
Author(s):  
Dario Grana
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Random Variables ◽  
Rock Physics ◽  
Petrophysical Properties ◽  
Kumaraswamy Distribution ◽  
Model Predictions ◽  
Physics Model ◽  
Rock Physics Model

Rock physics models are physical equations that map petrophysical properties into geophysical variables, such as elastic properties and density. These equations are generally used in quantitative log and seismic interpretation to estimate the properties of interest from measured well logs and seismic data. Such models are generally calibrated using core samples and well log data and result in accurate predictions of the unknown properties. Because the input data are often affected by measurement errors, the model predictions are often uncertain. Instead of applying rock physics models to deterministic measurements, I propose to apply the models to the probability density function of the measurements. This approach has been previously adopted in literature using Gaussian distributions, but for petrophysical properties of porous rocks, such as volumetric fractions of solid and fluid components, the standard probabilistic formulation based on Gaussian assumptions is not applicable due to the bounded nature of the properties, the multimodality, and the non-symmetric behavior. The proposed approach is based on the Kumaraswamy probability density function for continuous random variables, which allows modeling double bounded non-symmetric distributions and is analytically tractable, unlike the Beta or Dirichtlet distributions. I present a probabilistic rock physics model applied to double bounded continuous random variables distributed according to a Kumaraswamy distribution and derive the analytical solution of the posterior distribution of the rock physics model predictions. The method is illustrated for three rock physics models: Raymer’s equation, Dvorkin’s stiff sand model, and Kuster-Toksoz inclusion model.

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