Facies classification and rock physics model to understand the heterogeneity of the Waha reservoir properties in RG Field, Sirte Basin, Libya

2021 ◽  
Author(s):  
Yousf Abushalah ◽  
Laura Serpa ◽  
Abdalla Abdelnabi
Keyword(s):  
Rock Physics ◽  
Reservoir Properties ◽  
Facies Classification ◽  
Physics Model ◽  
Rock Physics Model ◽  
Sirte Basin

scholarly journals Rock Physics Modeling Assisted Reservoir Properties Prediction: Case Study in Malay Basin

2018 ◽  
Vol 7 (3.32) ◽  
pp. 24 ◽  
Author(s):  
Amir Abbas Babasafari ◽  
Deva Ghosh ◽  
Ahmed M. A. Salim ◽  
S Y. Moussavi Alashloo
Keyword(s):  
Oil And Gas ◽  
Rock Physics ◽  
Shear Velocity ◽  
Reservoir Modeling ◽  
Reservoir Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Well Log Analysis ◽  
Physics Model ◽  
Rock Physics Model

Shear velocity log is not measured at all wells in oil and gas fields, thus rock physics modeling plays an important role to predict this type of log. Therefore, seismic pre stack inversion is performed and elastic properties are estimated more accurately. Subsequently, a robust Petro-Elastic relationship arising from rock physics model leads to far more precise prediction of petrophysical properties. The more accurate rock physics modeling results in less uncertainty of reservoir modeling. Therefore, a valid rock physics model is intended to be built. For a better understanding of reservoir properties prediction, first of all rock physics modeling for each identified litho-facies classes should be performed separately through well log analysis.  

Seismic Reflections of Rock Properties in a Clastic Environment

2021 ◽  
Author(s):  
Vagif Suleymanov ◽  
Abdulhamid Almumtin ◽  
Guenther Glatz ◽  
Jack Dvorkin
Keyword(s):  
Rock Physics ◽  
Pore Fluid ◽  
Rock Properties ◽  
Petrophysical Properties ◽  
Sound Waves ◽  
Reservoir Properties ◽  
Field Guide ◽  
Physics Model ◽  
Rock Physics Model ◽  
Seismic Reflections

Abstract Generated by the propagation of sound waves, seismic reflections are essentially the reflections at the interface between various subsurface formations. Traditionally, these reflections are interpreted in a qualitative way by mapping subsurface geology without quantifying the rock properties inside the strata, namely the porosity, mineralogy, and pore fluid. This study aims to conduct the needed quantitative interpretation by the means of rock physics to establish the relation between rock elastic and petrophysical properties for reservoir characterization. We conduct rock physics diagnostics to find a theoretical rock physics model relevant to the data by examining the wireline data from a clastic depositional environment associated with a tight gas sandstone in the Continental US. First, we conduct the rock physics diagnostics by using theoretical fluid substitution to establish the relevant rock physics models. Once these models are determined, we theoretically vary the thickness of the intervals, the pore fluid, as well as the porosity and mineralogy to generate geologically plausible pseudo-scenarios. Finally, Zoeppritz (1919) equations are exploited to obtain the expected amplitude versus offset (AVO) and the gradient versus intercept curves of these scenarios. The relationship between elastic and petrophysical properties was established using forward seismic modeling. Several theoretical rock physics models, namely Raymer-Dvorkin, soft-sand, stiff-sand, and constant-cement models were applied to the wireline data under examination. The modeling assumes that only two minerals are present: quartz and clay. The appropriate rock physics model appears to be constant-cement model with a high coordination number. The result is a seismic reflection catalogue that can serve as a field guide for interpreting real seismic reflections, as well as to determine the seismic visibility of the variations in the reservoir geometry, the pore fluid, and the porosity. The obtained reservoir properties may be extrapolated to prospects away from the well control to consider certain what-if scenarios like plausible lithology or fluid variations. This enables building of a catalogue of synthetic seismic reflections of rock properties to be used by the interpreter as a field guide relating seismic data to volumetric reservoir properties.

scholarly journals Rational Rock Physics for Improved Velocity Prediction and Reservoir Properties Estimation for Granite Wash (Tight Sands) in Anadarko Basin, Texas

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Muhammad Z. A. Durrani ◽  
Keith Willson ◽  
Jingyi Chen ◽  
Bryan Tapp ◽  
Jubran Akram
Keyword(s):  
Rock Physics ◽  
Accurate Estimation ◽  
Complex Nature ◽  
Seismic Velocities ◽  
Reservoir Properties ◽  
Anadarko Basin ◽  
S Waves ◽  
Velocity Prediction ◽  
Physics Model ◽  
Rock Physics Model

Due to the complex nature, deriving elastic properties from seismic data for the prolific Granite Wash reservoir (Pennsylvanian age) in the western Anadarko Basin Wheeler County (Texas) is quite a challenge. In this paper, we used rock physics tool to describe the diagenesis and accurate estimation of seismic velocities of P and S waves in Granite Wash reservoir. Hertz-Mindlin and Cementation (Dvorkin’s) theories are applied to analyze the nature of the reservoir rocks (uncemented and cemented). In the implementation of rock physics diagnostics, three classical rock physics (empirical relations, Kuster-Toksöz, and Berryman) models are comparatively analyzed for velocity prediction taking into account the pore shape geometry. An empirical (VP-VS) relationship is also generated calibrated with core data for shear wave velocity prediction. Finally, we discussed the advantages of each rock physics model in detail. In addition, cross-plots of unconventional attributes help us in the clear separation of anomalous zone and lithologic properties of sand and shale facies over conventional attributes.

Rock-physics machine learning toolkit for joint litho-fluid facies classification and compaction modeling

2021 ◽  
Vol 40 (10) ◽  
pp. 742-750
Author(s):  
Roman Beloborodov ◽  
James Gunning ◽  
Marina Pervukhina ◽  
Kester Waters ◽  
Nick Huntbatch
Keyword(s):  
Machine Learning ◽  
Em Algorithm ◽  
Prior Information ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Borehole Data ◽  
Facies Classification ◽  
Physics Model ◽  
Rock Physics Model ◽  
Fitting Parameters

Correct lithofacies interpretation sourced from wireline log data is an essential source of prior information for joint seismic inversion for facies and impedances, among other applications. However, this information is difficult to interpret or extract manually due to the multivariate and high dimensionality of wireline logs. Facies inference is also challenging for traditional clustering-based approaches because pervasive compaction trends affect a number of petrophysical measurements simultaneously. Another common pitfall in automated clustering approaches is the inability to account for underlying diagenetic processes that correlate with depth. Here, we address these challenges by introducing a rock-physics machine learning toolkit for joint litho-fluid facies classification. The litho-fluid types are inferred from the borehole data within the objective framework of a maximum-likelihood approach for latent facies variables and rock-physics model parameters, explicitly accounting for compaction and depth effects. The inference boils down to an expectation-maximization (EM) algorithm with strong spatial coupling. Each litho-fluid type is associated with an instance of a particular rock-physics model with a unique set of fitting parameters, constrained to a physically reasonable range. These fitting parameters in turn are inferred using bound-constrained optimization as part of the EM algorithm. Outputs produced by the toolkit can be used directly to specify the necessary prior information for seismic inversion, including per-facies rock-physics models and facies proportions. We present an example application of the tool to real borehole data from the North West Shelf of Australia to illustrate the method and discuss its characteristic features in depth.

Stochastic inversion by matching to large numbers of pseudo-wells

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. M7-M22 ◽  
Author(s):  
Patrick A. Connolly ◽  
Matthew J. Hughes
Keyword(s):  
Rock Physics ◽  
Large Data ◽  
Data Sets ◽  
Stochastic Algorithm ◽  
Inversion Algorithm ◽  
Reservoir Properties ◽  
Geological Interpretation ◽  
Large Numbers ◽  
Physics Model ◽  
Rock Physics Model

We have developed a 1D stochastic algorithm for estimating reservoir properties, based on matching large numbers of pseudo-wells to seismic angle stacks. The pseudo-wells are part deterministic and part stochastic 1D stratigraphic profiles with consistent elastic and reservoir properties. Pseudo-wells are sampled from a prior distribution defined by the geological interpretation, a rock physics model and a model for the vertical statistics that provides close control of the lithofacies proportions. A new set of pseudo-wells, typically [Formula: see text] tied to the local stratigraphy, is constructed for each seismic trace. Synthetics, derived from the pseudo-wells using extended elastic impedance, are matched to either one or two seismic angle stacks, and the best matches are selected and averaged to provide a joint estimate of reservoir properties and impedances and the associated uncertainties. The algorithm has been tested on a number of data sets and validated by blind well ties. The algorithm is 1D with no additional constraints on spatial correlation beyond that provided by the seismic data. This restricts the maximum frequency to that of the seismic; however, it makes the algorithm highly parallelizable, allowing for large data sets to be inverted in a few hours given adequate computing resources. We envisage that this inversion algorithm could form the first part of a two-step process with the output used to constrain subsequent geostatistical modeling.

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.

Porosity estimation based on rock skeleton general formula and comprehensive pore structure parameter – An application to a tight-sand reservoir

2021 ◽  
pp. 1-59
Author(s):  
Kai Lin ◽  
Xilei He ◽  
Bo Zhang ◽  
Xiaotao Wen ◽  
Zhenhua He ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Pore Structure ◽  
Seismic Data ◽  
Rock Physics ◽  
Structure Parameters ◽  
Pore Structure Parameter ◽  
Elastic Parameters ◽  
Physics Model ◽  
Rock Physics Model ◽  
Porosity Estimation

Most of current 3D reservoir’s porosity estimation methods are based on analyzing the elastic parameters inverted from seismic data. It is well-known that elastic parameters vary with pore structure parameters such as pore aspect ratio, consolidate coefficient, critical porosity, etc. Thus, we may obtain inaccurate 3D porosity estimation if the chosen rock physics model fails properly address the effects of pore structure parameters on the elastic parameters. However, most of current rock physics models only consider one pore structure parameter such as pore aspect ratio or consolidation coefficient. To consider the effect of multiple pore structure parameters on the elastic parameters, we propose a comprehensive pore structure (CPS) parameter set that is generalized from the current popular rock physics models. The new CPS set is based on the first order approximation of current rock physics models that consider the effect of pore aspect ratio on elastic parameters. The new CPS set can accurately simulate the behavior of current rock physics models that consider the effect of pore structure parameters on elastic parameters. To demonstrate the effectiveness of proposed parameters in porosity estimation, we use a theoretical model to demonstrate that the proposed CPS parameter set properly addresses the effect of pore aspect ratio on elastic parameters such as velocity and porosity. Then, we obtain a 3D porosity estimation for a tight sand reservoir by applying it seismic data. We also predict the porosity of the tight sand reservoir by using neural network algorithm and a rock physics model that is commonly used in porosity estimation. The comparison demonstrates that predicted porosity has higher correlation with the porosity logs at the blind well locations.

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