Estimation Of Reservoir Properties Research Articles

Estimation of reservoir properties using pre-stack seismic inversion and neural network in mature oil field, Upper Assam basin, India

The mature oil fields require comprehensive characterization for enhanced hydrocarbon production, and subsequently demands estimation of reservoir properties. The key properties viz. volume of clay, effective-porosity, hydrocarbon-saturation has been evaluated for an aging Oligocene reservoir of Upper Assam basin, located in northeastern India from seismic and well log data. Elastic properties (acoustic and shear impedance) and density are derived from pre-stack inversion of 3D seismic data. These elastic properties are analyzed for their sensitivity for discrimination of lithology and fluid-content, and many derived attributes are computed from elastic properties. These attributes are assessed for their predictability to predict the target reservoir properties using multi-attribute analysis. For each of the target property neural network is trained with the most predictable attributes, and multi-dimensional, non-linear neural network models are created using multilayered feed forward neural network (MLFN), followed by Probabilistic neural network (PNN). The specific neural network models for each target property are employed for quantitative estimate of volume of clay, effective-porosity, hydrocarbon-saturation in inter-well regions. The estimated properties leverage the identification of untapped oil reserves and provide promising opportunity for enhanced production through drilling of infill wells.

Formation Evaluation and Petrophysical Analysis of Well Logging Data: an Example From Matruh-Shushan Basin, North Western Desert, Egypt

Petrophysical evaluation of well logging data is crucial for exploring and improving oil and gas reservoirs. This involves obtaining reliable information about the rocks and fluids in the reservoir to estimate reserves and make informed decisions about drilling, production, and development strategies. In this study, data from six wells in the Matruh-Shushan Basin were evaluated using Log Quality Control procedures. Two zones of interest, Lower and Upper Safa members, were identified, and quantitative interpretation was conducted to determine the most effective parameters defining reservoir quality: Shale Volume, Effective Porosity, and Water Saturation. Results were positive, with promising net pay for both reservoirs. Numerical models and statistical methods were used to account for data uncertainties and provide reliable estimates of reservoir properties. The study highlights the significance of correct interpretation in successfully exploring and producing hydrocarbons.

Time-lapse full-waveform permeability inversion: A feasibility study

Time-lapse seismic monitoring necessitates integrated workflows that combine seismic and reservoir modeling to enhance reservoir property estimation. We present a feasibility study of an end-to-end inversion framework that directly inverts for permeability from multiple prestack time-lapse seismic data sets. To assess the method's robustness, we design experiments focusing on its sensitivity to initial models, potential errors in modeling, and crosstalk during multiparameter inversion. Our study leverages the publicly available Compass model to simulate CO2 storage in saline aquifers. This model is derived from well and seismic data from the North Sea in an area that is currently considered for geologic carbon storage.

Novel approaches to uncertainty estimation in seismic subsurface characterization

Uncertainty estimation in subsurface characterization workflows is an important input to decision-making in earth science-related problems. We present three methods to characterize seismic-related uncertainty, each of which includes a real data case study. Two of these methods are designed to characterize depth uncertainty in positioning of migrated reflectors. Such estimates may be used in derisking well depth prognoses, analysis of well misties, and for estimating ranges on resource volume. The first method derives rapid and robust estimates of depth uncertainty around an existing velocity model using traditional velocity analysis to constrain the solution space while limiting a-priori constraints, consistent with a frequentist approach to uncertainty characterization. The second method characterizes depth uncertainty more rigorously in respect to the physics and prior information available by performing full-waveform inversion in a Bayesian framework. This method produces similar uncertainty estimates to the first in the case of simple velocity models but is more accurate where the overburden is complex; however, it requires significantly greater computational expense, thus limiting current practical applications. The third method is an amplitude variation with angle (AVA) inversion for reservoir properties designed to output uncertainty products for interpreters, utilizing the Bayesian framework and Zoeppritz equations to define the forward physics. Application to an offshore Egypt field demonstrates that it can generate reliable estimates of reservoir properties (e.g., lithofluid type or shale volume) including uncertainty, useful in various parts of subsurface characterization. These results also show that the method could provide improved point estimates of reservoir properties compared to conventional deterministic AVA inversion approaches. There is usually a trade-off between increasing the accuracy of subsurface characterization versus creating faster, less expensive, and more readily understood workflows for practitioners. We discuss how the relative importance of these competing factors should be considered within the context of how the outputs will be used.

Gas prediction in tight sandstones based on the rock-physics-derived seismic amplitude variation versus offset method

Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas reservoirs. However, the low sensitivity of elastic parameters to gas saturations in such formations makes it a significant challenge to reliably estimate gas enrichments using seismic methods. Through rock physical modeling and reservoir parameter analyses conducted in this study, a more suitable indicator for estimating gas enrichment, termed the gas content indicator, has been proposed. This indicator is formulated based on effective fluid bulk modulus and shear modulus and demonstrates a clear positive correlation with gas content in tight sandstones. Moreover, a new seismic amplitude variation versus offset (AVO) equation is derived to directly extract reservoir properties, such as the gas content indicator and porosity, from prestack seismic data. The accuracy of this proposed AVO equation is validated through comparison with the exact solutions provided by the Zoeppritz equation. To ensure reliable estimations of reservoir properties from partial angle-stacked seismic data, the proposed AVO equation is reformulated within the elastic impedance inversion framework. The estimated gas content indicator and porosity exhibit favorable agreement with logging data, suggesting that the obtained results are suitable for reliable predictions of tight sandstones with high gas enrichments. Furthermore, the proposed methods have the potential to stimulate the advancement of other suitable inversion techniques for directly estimating reservoir properties from seismic data across various petroleum resources.

A New Workflow for Estimating Reservoir Properties With Gradient Boosting Model and Joint Inversion Using MWD Measurements

Triple-combo logs are important measurements for estimating geological, petrophysical, and geomechanical properties. Unfortunately, wireline and advanced logging-while-drilling (LWD) logs are typically dropped from the formation evaluation plan for unconventional wells due to economic constraints or borehole instability risks. Available measurements are typically measurement-while-drilling (MWD) gamma ray (GR) logs, along with surface measurements such as weight on bit (WOB), rate of penetration (ROP), torque, rotation per minute (RPM), and differential pressure. The development of a robust and rapid model for predicting reservoir properties using this limited data set would be of high value for geological evaluation. Estimating such properties is a challenging task due to the nonlinear relationship between the available log data and unknown reservoir properties. A novel workflow that combines two sequential models is presented. First is a machine-learning (ML) algorithm to predict triple-combo logs from drilling dynamics and GR logs. To train the ML algorithm, well logs obtained from multiple wells located in the Eagle Ford and Permian Basins are scrutinized to identify important features. This process includes depth shifting, outlier detection, and feature selection, which allows for strategic hyperparameter tuning. Several regression algorithms are investigated, and it is found that gradient boosting algorithms yield superior prediction performance. Unlike random forest methods, boosting algorithms train predictors sequentially, each trying to correct its predecessor. After triple-combo logs are predicted from MWD logs, a physics-based joint inversion model is applied to estimate various reservoir properties. The trained model is deployed on a blind test well, and the predicted logs show excellent agreement compared to the corresponding triple-combo measurements. The multimineral inversion using predicted triple-combo logs yields a geologic model that is validated with elemental capture spectroscopy (ECS) measurements. Additionally, reconstructed logs from the forward model closely match measured logs by minimizing the cost function. Therefore, real-time estimated geological, petrophysical, and geomechanical properties can reveal complex geologic information and be used to mitigate uncertainty related to drilling optimization, reservoir characterization, development planning, and reserve estimation. Using the MWD logs to predict triple-combo logs followed by a joint inversion is an innovative approach for geological evaluation with a limited data set. The developed workflow can successfully provide (1) geologic lithofacies identification and rock typing, (2) more confidence in real-time drilling operation, (3) reservoir properties prediction, (4) missing log imputations and pseudo-log generation with forward modeling, (5) guidance for future logging and perforation, (6) reference for seismic quantitative interpretation (QI) and well tie, and (7) potentially massive computation time saving from days to minutes.

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

An accurate petrophysical model of the subsurface is essential for resource development and CO2 sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks). First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, Vclay, and fluid variations affect the elastic properties. In Poisson’s ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% Vclay decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Sleipner Øst field) was evaluated as a potential CO2 geological storage unit using Gassmann fluid substitution and seismic modeling. We look to assess the elastic property variation caused by CO2 saturation changes for monitoring purposes and simulate the effect. In the first 10% CO2 substitution, the P-wave velocity decrease is 12%, a subtle effect is observed for higher CO2 saturation values, and S-wave velocity (Vs) increases with CO2 saturation. Our analysis aspires to assist future reservoir studies and CO2 sequestration in similar fields.

Well-Log-Based Reservoir Property Estimation With Machine Learning: A Contest Summary

Well logs are processed and interpreted to estimate in-situ reservoir properties, which are essential for reservoir modeling, reserve estimation, and production forecasting. While the traditional methods are mostly based on multimineral physics or empirical formulae, machine learning provides an alternative data-driven approach that requires much less a-priori geological or petrophysical information. From October 2021 to March 2022, the Petrophysical Data-Driven Analytics Special Interest Group (PDDA SIG) of the Society of Petrophysicists and Well Log Analysts (SPWLA) hosted a machine-learning contest aiming to develop data-driven models for estimating reservoir properties, including shale volume, porosity, and fluid saturation, based on a common set of well logs, including gamma ray, bulk density, neutron porosity, resistivity, and sonic. Log data from nine wells from the same field, together with the interpreted reservoir properties by petrophysicists, were provided as training data, and five additional wells were provided as blind test data. During the contest, various data-driven models were developed by the contestants to predict the three reservoir properties with the provided training data set. The top five performing models from the contest, on average, beat the performance of the benchmarked Random Forest model by 45% in the root-mean-square error (RMSE) score. In the paper, we will review these top-performing solutions, including their preprocessing techniques, feature engineering, and machine-learning models, and summarize their advantages and conditions.

Well Production Decline Models Considering Mechanical Skin Factor

Production decline analysis is an essential tool in predicting reservoir performance and in estimating reservoir properties. In its most basic form, decline curve analysis is to a large extent based on Arps’ empirical models that have little theoretical basis. But the mechanical skin factor due to formation damage is not taken into account in the production decline models available in the literature. The conventional models presented in the literature are mostly empirical and not grounded in fundamental in theory. New theoretical models are developed in this paper to forecast the production decline of a well producing in a closed circular or rectangular reservoir at constant bottomhole pressure, and mechanical skin factor due to formation damage is taken into account. Both the transient flow behavior and boundary‐dominated flow behavior are incorporated; the effects of reservoir size, well location, and mechanical skin factor on transient flow rate and pressure drop in the reservoir are studied. Analytical solutions are obtained through combinations of Green’s function and inverse Laplace transform. Stehfest’s algorithm is used to convert the obtained solution from the Laplace transform domain into the real time domain. The proposed models in this paper are based on analytical solutions to diffusivity equations in porous media, and the solutions are fast and practical tools to forecast the production performance of a well producing at constant flowing bottomhole pressure.

Impacts of artificial dams on terrestrial water storage changes and the Earth's elastic load response during 1950–2016: A case study of medium and large reservoirs in Chinese mainland

The construction of dams for intercepting and storing water has altered surface water distributions, land-sea water exchanges, and the load response of the solid Earth. The lack of accurate estimation of reservoir properties through the land surface and hydrological models can lead to water storage simulation and extraction errors. This impact is particularly evident in many artificial reservoirs in China. The study aims to comprehensively assess the spatiotemporal distribution and trends of water storage in medium and large reservoirs (MLRs) in Chinese mainland during 1950–2016, and to investigate the gravity, displacement, and strain effects induced by the reservoir mass concentration using the load elasticity theory. In addition, the impoundment contributions of MLRs to the relative sea level changes were assessed using a sea-level equation. The results show impoundment increases in the MLRs during 1950–2016, particularly in the Yangtze River (Changjiang) and southern basins, causing significant elastic load effects in the surrounding areas of the reservoirs and increasing the relative sea level in China's offshore. However, long-term groundwater estimation trends are overestimated and underestimated in the Yangtze River and southwestern basins, respectively, due to the neglect of the MLRs impacts or the uncertainty of the hydrological model's output (e.g., soil moisture, etc.). The construction of MLRs may reduce the water mass input from land to the ocean, thus slowing global sea level rise. The results of the impact of human activities on the regional water cycle provide important references and data support for improving the integration of hydrological models, evaluating Earth's viscoelastic responses under long-term reservoir storage, enhancing in-situ and satellite geodetic measurements, and identifying the main factors driving sea level changes.

Qualitative and quantitative diagenetic modelling in a tight carbonate reservoir in north-western Iraq

The diagenetic history of the Butmah Formation (Lower Jurassic) is very complex and affected by several diagenetic processes that worked effectively with fracturing control to create the final pore network. A microscopic study, core plug and well log analyses were combined in this study in order to describe, and differentiate between, the diagenetic and fracturing control that created the final pore system of the formation. The diagenetic processes of the Butmah Formation were studied in depth to describe the diagenetic stages and identify the elements that may compose a petrodiagenetic pathway illustrating its effect on the reservoir quality of the Butmah Formation. Accordingly, the Butmah Formation samples were divided into three petrophysical fields controlled mainly by fracturing and diagenesis, which were then used to develop a new method for estimating the pre-dolomitisation petrophysical properties of the dolomite samples and the post-dolomitisation petrophysical properties of the limestone samples. Consequently, the output of applying this method allows us to effectively begin to predict each of the elements that may compose a petrodiagenetic pathway for the Butmah Formation and make its reservoir characterisation integrated and more understandable. The new method provided a good prediction of matrix porosity and permeability, as well as allowing the estimation of reservoir properties of any other carbonate reservoir in petroleum development projects when there are no core samples in some formation intervals within boreholes.

Diagnostics of gas-dynamic connection between gas caps and their influence on the product composition of the North Caspian production wells as per tracer tests

Diagnostics of gas-dynamic connection between gas caps and their influence on the product composition of the North Caspian production wells as per tracer tests

Fast Assisted History Matching of Fractured Vertical Well in Coalbed Methane Reservoirs Using the Bayesian Adaptive Direct Searching Algorithm

The proper understanding of reservoir properties is an important step prior to forecasting fluid productions and deploying development strategies of a coalbed methane (CBM) reservoir. The assisted history matching (AHM) technique is a powerful technique that can derive reservoir properties based on production data, which however is usually rather time-consuming because hundreds or even thousands of numerical simulation runs are required before reasonable results can be obtained. This paper proposed the use of a newly developed algorithm, namely the Bayesian adaptive direct searching (BADS) algorithm, for assisting history matching of fractured vertical CBM wells to derive reservoir property values. The proposed method was applied on representative fractured vertical wells in the low-permeable CBM reservoirs in the Qinshui Basin, China. Results showed that the proposed method is capable of deriving reasonable estimates of key reservoir properties within a number of 50 numerical simulation runs, which is far more efficient than existing methods. The superiority of the BADS algorithm in terms of matching accuracy and robustness was highlighted by comparing with two commonly used algorithms, namely particle swarm optimization (PSO) and CMA-ES. The proposed method is a perspective in laboring manual efforts and accelerating the matching process while ensuring reasonable interpretation results.

The application of the PNN algorithm to evaluate the petrophysical properties of the heterogeneous E-sand reservoirs, Lower Goru Formation, Pakistan

The Lower Goru Formation (LGF) has produced from E-sand in Kadanwari gas field, composed of sand and shale. The estimation of reservoir properties, including porosity, mineralogy, and thickness, which could help to identify new possible drilling sites, is hindered by the heterogeneous nature of LGF, and intermix shale, and sand. Integration of elastic and petrophysical properties worked as an effective tool for evaluating reservoir properties, especially for handling the heterogeneities of sand reservoirs associated with pores and their fluids. The probabilistic neural networking (PNN) technique has been proven the most effective and is a widely applied algorithm for predicting petrophysical parameters away from control points. It can create nonlinear correlations between input and estimated properties by efficiently handling the intermix shale. For this study, PNN prediction demonstrated a reliable statistical approximation. The trends of the actual and predicted properties such as effective porosities (PHIE ​= ​∅e) and water saturation (Sw) for E-sand level exhibited an excellent correlation of 0.98 and 0.99 with an error rate of 0.006 and 0.03 respectively, which is also evident through the regression lines. The promising zones of the reservoir are further assessed by the multivariate seismic attributes, i.e., amplitudes, sweetness, and RGBA blending of appropriate frequency bands, which illuminated the potential channelized sands. The methodology applied in this study eventually minimized the uncertainty associated with petrophysical properties such as ∅e and Sw of the producing sands in LGF. The findings suggest that when dealing with heterogeneous reservoirs, a similar approach could be employed to locate feasible prospects.

Electromagnetic resistivity pseudo-log as a new instrument for estimating reservoir properties beyond boreholes

A new approach to the reservoir properties' prediction using so called “electromagnetic resistivity pseudo-log” built in the target location is suggested. It is based on artificial neural network continuation of resistivity well logs beyond boreholes using electromagnetic resistivity profiles determined from adjacent ground electromagnetic survey. As a result of this transformation, we obtain a resistivity profile which possesses high resolution of the borehole resistivity log in the reservoir scale. We demonstrate advantages of this approach by neural network modelling of the porosity prediction below boreholes and in the interwell space. To this end we use electrical logging data collected in two boreholes located in the Northern Tien Shan and results of magnetotelluric survey in their vicinity. It is shown that porosity prediction using the developed algorithm provides better results than its estimation using resistivity logs or electromagnetic resistivity alone. In particular, the mean relative error can be as low as ∼2% in the case of predicting at double borehole depth and around 8% in the interwell space.

Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability

To maximize the economic benefits of geothermal energy production, it is essential to optimize geothermal reservoir management strategies, in which geologic uncertainty should be considered. In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs. In this framework, we construct a hybrid convolution–recurrent neural network surrogate, which combines the convolution neural network (CNN) and long short-term memory (LSTM) recurrent network. The convolution structure can extract spatial information of reservoir property fields and the recurrent structure can approximate sequence-to-sequence mapping. The trained model can predict time-varying production responses (rate, temperature, etc.) for cases with different permeability fields and well control sequences. In this closed-loop optimization framework, production optimization, based on the differential evolution (DE) algorithm, and data assimilation, based on the iterative ensemble smoother (IES), are performed alternately to achieve a real-time well control optimization and to estimate reservoir properties (e.g. permeability) as the production proceeds. In addition, the averaged objective function over the ensemble of geologic parameter estimates is adopted to consider geologic uncertainty in the optimization process. Geothermal reservoir production cases are examined to evaluate the performance of the proposed closed-loop optimization framework. Our results show that the proposed framework can achieve efficient and effective real-time optimization and data assimilation in the geothermal reservoir production process.

Stage-by-Stage Hydraulic Fracture and Reservoir Characterization through Integration of Post-Fracture Pressure Decay Analysis and the Flowback Diagnostic Fracture Injection Test Method

Summary The post-fracture pressure decay (PFPD) technique is a low-cost method allowing for stage-by-stage hydraulic fracture characterization. The analysis of the PFPD data is complex, with data affected by both hydraulic fracture and reservoir properties. Available analysis methods in the literature are oversimplified; reservoir or fracture properties are often assumed to be constant along the horizontal well, and therefore changes in the trend of pressure decay data are attributed to hydraulic fracture or reservoir properties only. Moreover, methods analogous to those applied to the analysis of conventional diagnostic fracture injection tests (DFITs) are often used and ignore critical mechanisms involved in main-stage hydraulic fracture stimulation. A conceptual numerical simulation study was first conducted herein to understand the key mechanisms involved in main-stage hydraulic fracturing. An analytical model was then developed to account for the dynamic behavior of the hydraulic fracture, leakoff, proppant distribution, multiple fractures, and propped- and unpropped-closure events. The analytical model is cast in the form of a new straightline analysis (SLA) method that provides stage-by-stage estimates of the ratio of unpropped fracture surface area to total fracture surface area. The SLA method was validated against numerical simulation results. Moreover, to account for the variation of reservoir properties along the horizontal well, the PFPD model is integrated with DFIT-flowback (DFIT-FBA) tests, performed at some points along the lateral, to obtain a reliable stage-by-stage hydraulic fracture and reservoir characterization approach. The practical application of the proposed integrated approach was demonstrated using PFPD and DFIT-FBA data from a horizontal well completed in 22 stages in the Montney Formation. The numerical simulation study demonstrated that the use of proppant and injection into multiple clusters (creating multiple fractures) results in multiple closure events. The closure process may start early after the pump-in period at a pressure significantly higher than the minimum in-situ stress. Using DFIT-based analytical models, which ignore the presence of proppant, causes significant errors in hydraulic fracture and reservoir property estimation. The PFPD field data examined herein exhibited a similar pressure trend to the numerical simulation cases. The ratio of unpropped fracture surface area to total fracture surface area was determined stage by stage using the PFPD SLA method, constrained by DFIT-FBA data. Engineers can use this information to optimize the hydraulic fracture stimulation design in real time, optimize the well spacing, and forecast the production. The cost and time advantages of this diagnostic method make this approach very attractive.

Seismic rock-physics linearized inversion for reservoir-property and pore-type parameters with application to carbonate reservoirs

Carbonate reservoirs hold promising potential for hydrocarbon exploration, but they usually exhibit complex pore structures, which lead to difficulties in the seismic/elastic prediction via conventional rock physics modeling, and in turn, hinder an accurate estimation of reservoir properties from seismic observations. This work presents a seismic rock-physics inversion method to jointly estimating reservoir-property (porosity and fluid saturation) and pore-type (pore aspect ratio) parameters in carbonates from seismic/elastic data. The method considers the aspect ratio as a pending parameter, which favors the pore-type variation so as to describe the structure complexity. To achieve the joint estimation, we derive a linearized rock physics model based on the differential effective medium model and the Biot-Gassmann equation that relate porosity, water saturation, and aspect ratio to elastic parameters. Moreover, to realize the estimation with computational efficiency, we derive the analytical solution to the inversion with the linearized model based on the Bayesian inverse theory. We estimate the pore aspect ratio at a well location to provide prior information for the inversion and employ an iterative inversion strategy with piecewise linearization to improve the result. The proposed method is applied to two carbonate reservoirs including 2D and 3D field data sets. Compared with the conventional rock physics inversion scheme with a constant aspect ratio, the application demonstrates that the method achieves better accuracy for reservoir property estimation, which can be a good indicator for the potential target areas of complex reservoirs.

Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model

AbstractGeophysical monitoring of geologic carbon sequestration is critical for risk assessment during and after carbon dioxide (CO2) injection. Integration of multiple geophysical measurements is a promising approach to achieve high‐resolution reservoir monitoring. However, joint inversion of large geophysical data is challenging due to high computational costs and difficulties in effectively incorporating measurements from different sources and with different resolutions. This study develops a differentiable physics model for large‐scale joint inverse problems with reparameterization of model variables by neural networks and implementation of a differentiable programming approach of the forward model. The proposed physics‐informed neural network model is completely differentiable and thus enables end‐to‐end training with automatic differentiation for multi‐objective optimization by multiphysics data assimilation. The application to the Sleipner benchmark model demonstrates that the proposed method is effective in estimation of reservoir properties from seismic and resistivity data and shows promising results for CO2 storage monitoring. Moreover, the global parameters that are assumed to be uncertain in the rock‐physics model are accurately quantified by integration of a Bayesian neural network.

Multi-Well Characterisation of Complex Carbonate Reservoirs Using S-Wave Impedance and P-Wave Impedance: Mission Canyon Carbonates (Mississippian), Williston Basin, US, as a Test Case

The complexity of carbonate reservoirs is, in part, caused by the multiple rock-types and porosity types that may occur in any carbonate reservoir. Consequently, a persistent concern has been how to derive accurate quantitative estimates of carbonate reservoir properties from borehole and seismic data. Recent developments in this pursuit involve combined use of S-wave and P-wave data, together with petrophysical definitions of their impedances in a manner that lends them geologic character and significance. S-wave impedance and P-wave impedance are linearly related with their respective velocities. Both relationships are rock-typing criteria since their slope and intercept terms consist of geology-dependent coefficients. Linear-linear crossplots of S-wave impedance versus S-wave velocity and P-wave impedance versus P-wave velocity, using core scale measurements in four wells, support the linearity, pattern of data points, and character of the lines predicted for brine and air/gas pore fluids. The slopes and intercepts obtained by the method of linear regression analysis also show values that are location-specific, as well as exhibit variabilities that are indicative of reservoir heterogeneities from one well to another. These results show strong potential in using impedance-velocity relationships for quantitative interpretations of well logs and reflection seismic data.