Mind the Gap Between Petrophysics and Rock Physics: Petro-Elastic Facies for Driving Reservoir Modeling

Carrasquero, G.; Fervari, M.; Tagliamonte, R.L.; Tarchiani, C.

doi:10.3997/2214-4609.2020603019

Cited by 3 publications

(1 citation statement)

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“…PEMs characterize the reservoirs' identified litho-facies by modeling the precise elastic responses from their petrophysical properties (Alfred et al, 2008;Mavko et al, 2020). The major outcomes of PEMs included Vp, Vs, ρ and their combinations, compensating for poor hole conditions, insensitivity of the recording tool, missing elastic logs especially s-wave, and ultimately featuring a good separation of the pay and nonpay facies in the elastic domain (Yusoff et al, 2014;Zeb and Murrell, 2015;Carrasquero et al, 2020). Based on in-situ reservoir properties, the elastic responses (Vp, Vs, and ρ) of shale, limestone, wet, and gas sands are simulated (Singha and Chatterjee, 2017).…”

Section: Petro-elastic Modelsmentioning

confidence: 99%

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

et al. 2022

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A challenging situation in reservoir characterization arises when reservoirs occur below the vertical resolution of seismic, such as the 6 m gas-prone sands of the heterogenous Khadro Formation. This formation is the Zamzama gas field's secondary reservoir and needs detailed evaluation for optimum production of the field as the deeper primary reservoir of Pab sandstone is facing the problem of early water encroachment in its producing wells. The characterization of thin heterogeneous gas-bearing facies is hampered by the insensitivity of well-bore recording tools in precisely capturing elastic properties. Consequently, petro-elastic models (PEMs) of identified facies are generated that produce consistent elastic responses and discriminate facies in their true elastic domains. The resolution of seismic elastic properties is enhanced to illuminate the thin gas-bearing sands by adopting a Bayesian stochastic inversion process in collaboration with PEMs modeled elastic responses on a fine-scale stratigraphic grid. In addition, the Bayesian framework is used to estimate the litho-facies probability cubes to mitigate the drilling risks by integrating highly sampled posterior elastic volumes, modeled elastic logs, and geologic information of identified litho-facies. The outcomes included prospect identification at the drilled well locations that need to be perforated, while new potential zones are present for additional wells.

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Section: Petro-elastic Modelsmentioning

confidence: 99%

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

et al. 2022

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Discrimination of lithofacies in tight gas reservoir using field-specific rock physics modeling scheme. A case study from a mature field of middle Indus Basin, Pakistan

et al. 2023

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Real-Time Rock-Properties Estimation for Geosteering: Statistical Rock-Physics-Driven Inversion of Seismic Acoustic Impedance and LWD Ultradeep Azimuthal Resistivity

Ciabarri

Tarchiani

Alberelli

et al. 2023

SPE Reservoir Evaluation &Amp; Engineering

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Summary This work describes a statistical rock-physics-driven inversion of seismic acoustic impedance (AI) and ultradeep azimuthal resistivity (UDAR) log data, acquired while drilling, to estimate porosity, water saturation, and facies classes around the wellbore. Despite their limited resolution, seismic data integrated with electromagnetic resistivity log measurements improve the description of rock properties by considering the coupled effects of pore space and fluid saturation in the joint acoustic and electrical domains. The proposed inversion does not explicitly use a forward model, rather the correlation between the petrophysical properties and the resulting geophysical responses is inferred probabilistically from a training data set. The training set is generated by combining available borehole information with a statistical rock-physics modeling approach. In the inversion process, given colocated measurements of seismic AI and logging-while-drilling (LWD) electromagnetic resistivity data, the pointwise probability distribution of rock properties is derived directly from the training data set by applying the kernel density estimation (KDE) algorithm. A nonparametric statistical approach is used to approximate nonsymmetric volumetric distributions of petrophysical properties and to consider the characteristic nonlinear relationship linking water saturation with resistivity. Given an a priori facies classification template for the samples in the training set, it is possible to model the multimodal, facies-dependent behavior of the petrophysical properties, together with their distinctive correlation patterns. A facies-dependent parameterization allows the effect of lithology on acoustic and resistivity responses to be implicitly considered, even though the target properties of inversion are only porosity and saturation. To provide a realistic uncertainty quantification of the estimated rock properties, a plain Bayesian framework is described to account for rock-physics modeling error and to propagate seismic and resistivity data uncertainties to the inversion results. In this respect, the uncertainty related to the scale difference among the well-log data and seismic is addressed by adopting a scale reconciliation strategy. The main feature of the described inversion lies in its fast implementation based on a look-up table that allows rock properties, with their associated uncertainty, to be estimated in real time following the acquisition and inversion of UDAR data. This gives a robust, straightforward, and fast approach that can be effortlessly integrated into existing workflows to support geosteering operations. The inversion is validated on a clastic oil-bearing reservoir, where geosteering was used to guide the placement of a horizontal appraisal well in a complex structural setting. The results show that the proposed methodology provides realistic estimates of the rock-property distributions around the wellbore to depths of investigation of 50 m. These constitute useful information to drive geosteering decisions and can also be used, post-drilling, to update or optimize existing reservoir models.

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Mind the Gap Between Petrophysics and Rock Physics: Petro-Elastic Facies for Driving Reservoir Modeling

Cited by 3 publications

References 0 publications

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

Discrimination of lithofacies in tight gas reservoir using field-specific rock physics modeling scheme. A case study from a mature field of middle Indus Basin, Pakistan

Real-Time Rock-Properties Estimation for Geosteering: Statistical Rock-Physics-Driven Inversion of Seismic Acoustic Impedance and LWD Ultradeep Azimuthal Resistivity

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