Uncertainty In Porosity Research Articles

Constraint optimization of an integrated production model utilizing history matching and production forecast uncertainty through the ensemble Kalman filter

The calibration of reservoir models using production data can enhance the reliability of predictions. However, history matching often leads to only a few matched models, and the original geological interpretation is not always preserved. Therefore, there is a need for stochastic methodologies for history matching. The Ensemble Kalman Filter (EnKF) is a well-known Monte Carlo method that updates reservoir models in real time. When new production data becomes available, the ensemble of models is updated accordingly. The initial ensemble is created using the prior model, and the posterior probability function is sampled through a series of updates. In this study, EnKF was employed to evaluate the uncertainty of production forecasts for a specific development plan and to match historical data to a real field reservoir model. This study represents the first attempt to combine EnKF with an integrated model that includes a genuine oil reservoir, actual production wells, a surface choke, a surface pipeline, a separator, and a PID pressure controller. The research optimized a real integrated production system, considering the constraint that there should be no slug flow at the inlet of the separator. The objective function was to maximize the net present value (NPV). Geological data was used to model uncertainty using Sequential Gaussian Simulation. Porosity scenarios were generated, and conditioning the porosity to well data yielded improved results. Ensembles were employed to balance accuracy and efficiency, demonstrating a reduction in porosity uncertainty due to production data. This study revealed that utilizing a PID pressure controller for the production separator can enhance oil production by 59% over 20 years, resulting in the generation of 2.97 million barrels of surplus oil in the field and significant economic gains.

Hypoplastic modeling post-failure behavior of sandy slope with porosity spatial variability

Recent probabilistic studies have thoroughly examined how uncertainties in soil shear strength affect slope stability, while the impact of spatial variability in soil porosity on post-failure behaviors of slopes has received less attention. Overlooking the influence of porosity may lead to inaccurate assessments of slope post-failure behaviors. This study presents a random smoothed particle hydrodynamics (RSPH) framework for probabilistic analysis of slope failure with large deformation using a critical state-based hypoplastic constitutive model. The results indicate that the proposed RSPH method provides reasonable estimates for slope post-failure behaviors. Increases in both the coefficient of variation (COV) and the scale of fluctuation (SOF) of porosity give rise to underestimations of the run-out distance and sliding volume. Additionally, slope failure modes are affected by increasing uncertainty of porosity, which may contribute to the occurrence of deep-seated failures.

Stochastic simulation of magnetically controlled convection in porous media with random porosity via Karhunen-Loève expansion and intrusive polynomial chaos expansion

Understanding and quantifying uncertainty factors are crucial for accurately predicting thermomagnetic convection phenomena. This study presented a mathematical model and algorithm framework for uncertainty analysis of thermomagnetic convection in porous media with random porosity. The proposed method combined Karhunen-Loève expansion and intrusive polynomial chaos expansion to represent the input random parameters and output response, respectively. The Galerkin projection method was employed to decouple the stochastic governing equations of thermomagnetic convection into deterministic governing equations that can be efficiently solved using the finite volume method. By solving the polynomial chaos expansion of the output response and employing the stochastic projection method to solve the associated decoupled governing equations, the temporal evolution and statistical characteristics of the output response were obtained. The study revealed that porosity uncertainty in the porous medium affects the thermomagnetic convection of paramagnetic fluid, exhibiting significant chaos effects. Monte Carlo simulations were performed to validate the accuracy of the proposed approach and demonstrate its computational efficiency compared to traditional Monte Carlo methods. This research provides valuable insights into the uncertainty analysis of thermomagnetic convection and offers a promising methodology for analyzing heat transfer in various porous media applications.

Machine learning–based optimal design of the in-situ leaching process parameter (ISLPP) for the acid in-situ leaching of uranium

The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights.

Assessment of adsorption materials for carbon capturing and storage in the context of carbon neutrality

In order to avoid the harm caused by global warming, carbon capture and store (CCS) technology has become a global hot topic. CO2 adsorption materials are an important basis for the realization of CCS. This paper explores the CO2 adsorption mechanisms of the currently widespread shale reservoirs, metal-organic framework (MOF), covalent-organic framework (COF), activated carbon and evaluated the relevant factors affecting their adsorption capacity. Through the exchange of CO2 and CH4, shale reservoir is a natural material with great CO2 adsorption and storage potential. However, due to the high implementation cost and the uncertainty of porosity, this technology is considered to be immature. MOF and COF have different chemical bonds, but they are both crystalline compounds with tunable porosity and internal surface area. They are very ideal CO2 adsorption materials. However, the main disadvantage of MOF and COF lies in their instability. Moreover, activated carbon is considered to be the adsorption material with the lowest production cost among the four. The adsorption performance of activated carbon is related to its raw materials, internal surface area, pore properties and external functional groups. However, activated carbon ashes are likely to cause secondary environmental pollution. In the future, it is necessary to improve the prediction accuracy of the adsorption potential of shale reservoirs, solve the unstable characteristics of MOF and COF, and maximize the adsorption performance of activated carbon in an environmentally friendly manner. The significance of this article is to provide theoretical support for the optimization and development of physical adsorption materials in the future.

A semi-analytical approach to nonlinear multi-scale seepage problem in fractured shale gas reservoirs with uncertain permeability distributions

To investigate the influence of the inhomogeneity and uncertainty of porosity and permeability on gas production, a mathematical model for shale gas nonlinear seepage problem with random distributions of inhomogeneous equivalent porosity and permeability is established. Based on the statistical characteristic of the artificial fracture density distribution and incompletely controllable hydraulic fracturing technology, the random distribution models of continuous inhomogeneous equivalent porosity and permeability are proposed to simulate the uncertainty of the porosity and permeability distribution in SRV. Coupled with multi-scale flow and adsorption effects, a mathematical model of shale gas nonlinear seepage problem is presented to address the inhomogeneity and uncertainty of equivalent porosity and permeability. By Boltzmann transformation and local homogenization approximation, a semi-analytical method and the corresponding explicit iterative scheme are developed.Simulation results match well with field data, which verifies the validity of the presented mathematical model and approach. The morphology of the SRV has significant influence on gas production, followed by the uncertainly of the permeability distribution and the hydraulic fracture half-length. When the horizontal width reaches its maximum in the front part of the macro-fracture zone, shale gas production reaches its peak and increases by 60%. The uncertainty of the permeability distribution is resulted from the deviation of the hydraulic fracture. As the deviation angle increases from 0° to 10°, shale gas production drops by 16%. The greater the gas production, the bigger the drop rate. When the hydraulic fracture half-length is 50 m, gas production reaches its peak, however it drops by 13% if the hydraulic fracture half-length increases to 70 m.

Porosity prediction using semi-supervised learning with biased well log data for improving estimation accuracy and reducing prediction uncertainty

SUMMARY Porosity characterization is of profound significance for seismic inversion and hydrocarbon prediction. Although semi-supervised learning (SSL) based methods have been used to boost prediction accuracy and lateral continuity of supervised learning (SL) inverted subsurface properties, their variations are relatively limited since the relationships between the data and the parameter model are straightforward in most reported cases. To further figure out their essential differences, we proposed the SSL-based network (SSLBN) for reservoir porosity prediction using seismic and well log data with disparate complexity and quality, and compared it with the SL-based network (SLBN). The SSLBN comprises a data-driven inverse model named decoder and a data-driven forward model named encoder based on the bidirectional-gated recurrent units. The architecture of the SLBN is the same as the encoder. Trained by several seismic-to-well pairs and numerous unlabelled seismic logs, the SSLBN learns the physical process from input single-trace observed seismic log to the intermediate porosity log, and the inverted porosity to the output generated seismic log. We first prepare the porosity model with biased or unbiased labels, the convolution model (CM) and reverse time migration (RTM) based synthetic seismic data, and then implement SL- and SSL-based statistical tests. The synthetic data examples demonstrate that the SSLBN has significant preponderance over the SLBN in the scenes of the RTM imaged seismic data and biased porosity labels. Compared with the SLBN, the physical regularization of the data misfit in the SSLBN improves estimation accuracy and reduces prediction uncertainty of porosity. Finally, statistical tests on a braided river deposited field data example illustrate that the SSLBN can generate more geologically trustworthy porosity models and indicate the oil layers of high porosity sandstone reservoirs.

Investigating the capability of data-driven proxy models as solution for reservoir geological uncertainty quantification

Quantifying and analyzing the geological uncertainties associated with the geostatistical models is critical to design a reliable production strategy. A large number, hundreds, even thousands, of model realizations should be run in a reservoir simulator to quantify the uncertainties in terms of a target flow response. However, this becomes a time consuming and computationally demanding process. In practice, different methods are used to select only a few representative models for further analyses. This study aims to take a fresh look at uncertainty quantification solutions by means of a proxy model based on artificial neural networks (ANN). The idea is replacing the flow simulator with an ANN-based proxy model by which the target responses of all realizations are calculated much faster and with a lower computational demand. The ability of computing the combined effect of several uncertain parameters along with successful experiences of utilizing ANNs for reservoir simulation, motivated us to propose our ANN-based methodology as a solution for uncertainty quantification problem. We apply our methodology on a realistic 3D synthetic model which is a channelized model with uncertainties in porosity, permeability, and facies properties. The results are compared with those of two widely used methods, i.e., the traditional ranking and distance-based clustering techniques. It is shown that the ANN-based proxy is able to accurately quantify the geological uncertainties in a much shorter time. Hence, it can be used as a reliable tool for uncertainty quantification and representative models selection. • A new solution for quantifying the geological uncertainties of reservoir models is presented. • A data-Driven proxy model based on Artificial Neural Networks is built to replace the comprehensive reservoir simulator. • The Data-Driven proxy model calculates the desired target response much faster. • The proposed solution outperforms the widely-used uncertainty quantification techniques.

Pore-Structure Characterization of a Complex Carbonate Reservoir in South Iraq Using Advanced Interpretation of NMR Logs

The Rumaila Field is in southeast Iraq and contains multiple reservoir intervals, including the Upper Cretaceous Mishrif carbonate reservoir, one of the major reservoirs in the world, that has been producing for more than 50 years. One of the key challenges in the Mishrif is to characterize the pore-structure distinction between primary and secondary porosity. The secondary porosity in the form of large pores, if present, dominates the petrophysical properties, especially permeability. Advanced logs, e.g., nuclear magnetic resonance (NMR) and image logs, can be used to understand the variations in pore structure, both qualitatively and quantitatively. In this paper, we focused primarily on four new wells with very comprehensive logging and coring programs. NMR logs were acquired using different tools and pulse sequences. This resulted in uncertainty in porosity and T2 distributions and, consequently, complications in the NMR interpretation. We observed two key issues: porosity deficit due to lack of polarization and T2 distribution truncation due to the low number of echoes. We used a single pore model to reproduce the NMR response in different pore sizes and fluid types for different pulse sequences. The results showed that the NMR response, especially in water-filled (water-based-mud filtrate) large pores, is sensitive to polarization time, echo spacing, and tool gradient strength. NMR log data confirmed the modeling results. We recommended an optimum pulse sequence and tool characteristics to fully capture the heterogeneous rock and fluid system in this carbonate reservoir. NMR logs, when available, were the primary tools to identify the large pores. We present a consistent workflow for NMR log analysis that was developed to identify and quantify large pores and extended to all wells in the field. We used advanced NMR interpretation techniques, e.g., factor analysis (NMR FA) (Jain et al., 2013), in a series of oil wells drilled with water-based mud. Using factor analysis, we identified a cutoff value of 847 ms for large pore volumes. In this manuscript, we also present an integration of laboratory measurements, e.g., NMR, mercury intrusion capillary pressure (MICP) data, whole-core CT scanning, and thin-section analysis, in our interpretation workflow. We also compared the large pore volume from image logs with NMR logs and other laboratory data and observed very consistent results. All the available information was integrated to build an “NMR-based” petrophysical model for porosity, rock type, permeability, and saturation determination. The NMR-based model was very comparable with the classic flow zone indicator (FZI) rock typing. The results of this study were used to modify the NMR acquisition program in the field and to build a petrophysical model based on only NMR and image log measurements for carbonate reservoirs. In this paper, we will discuss NMR modeling and corresponding log data from various wells to confirm the results. Furthermore, we will present a novel interpretation workflow integrating laboratory measurements and log data, which led to the modification of the NMR acquisition program in the field and the creation of a data-driven petrophysical model based on only NMR and image log measurements for carbonate reservoirs.

Emulation of Community Land Model Version 5 (CLM5) to Quantify Sensitivity of Soil Moisture to Uncertain Parameters

AbstractLand surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.

Bayesian deep networks for absolute permeability and porosity uncertainty prediction from image borehole logs from brazilian carbonate reservoirs

Image wirelogs, both acoustic and resistivity, encapsulate information regarding the studied reservoir's petrophysical properties that are used to pursue a meaningful characterization of it. More recently, some authors used those image logs to estimate porosity and permeability using a traditional image processing pipeline or using a deep learning perspective. Among others, those quantities might be critical to determine the feasibility of a specific hydrocarbon reservoir exploitation. However, the use of such estimates are limited since it is not possible to assign uncertainty on those deterministic methods. Thus, their reliability can not be assessed. In this work, we propose a novel approach using Deep Learning and Bayesian methodologies to address the estimation of uncertainty quantification for both permeability and porosity simultaneously from the image logs. The current methodology can be used to derive probability density functions (PDFs) from each prediction. As a result, the PDFs were used to estimate the robustness and uncertainties of the predicted values. The current method achieved a correlation coefficient R2 higher than 99% in both porosity and permeability regression in the blind test sample, i.e., using a different set of data that was not applied in the training procedure.

Interval mineral and fluid densities estimation from well-logs: Application to the Norne Field dataset

Formation evaluation techniques are the key to understand subsurface rocks properties from well-logs, especially those drilled in hydrocarbon exploration wells. Knowledge of the parameters related to different types of rocks is traditionally used in forward determination of lithology, porosity and water saturation, which can be refined by calibrating the input models. In this work, we perform well-log interval linear inversion with respect to formation density to investigate mineral and fluid properties in a real dataset. The method is based on an overdetermined problem, which supposes a homogeneous distribution of petrophysical parameters through stratigraphic layers and is applied in conventional reservoir rocks from the Norne Field (offshore Norway). Bulk density, gamma-ray and neutron porosity logs are employed in a workflow that relies on layer-by-layer least-squares regressions to estimate matrix, shale and fluid apparent densities. In this process, shale volume and the total density porosity are calculated depth by depth from empirical equations feed by the input logs. Therewith, the introduced inversion scheme stands as an alternative approach for well data interpretation that focuses on computing the densities of the rock constituents instead of fixing these parameters to invert fractional volumes. Furthermore, the application in two wellbores resulted in geological consistent individual densities in most intervals, except for a gas-bearing zone observed in one of the boreholes, where porosity uncertainty caused anomalous variation in grain and fluid densities.

Reservoir Porosity Measurement Uncertainty and its Influence on Shale Gas Resource Assessment

AbstractReservoir porosity is a critical parameter for the process of unconventional oil and gas resources assessment. It is difficult to determine the porosity of a gas shale reservoir, and any large deviation will directly reduce the credibility of any shale gas resources evaluation. However, there is no quantitative explanation for the accuracy of porosity measurement. In this paper, measurement uncertainty, an internationally recognized index, was used to evaluate the results of porosity measurement of gas shale plugs, and its impact on the credibility of shale gas resources assessment was determined. The following conclusions are drawn: (1) the measurement uncertainty of porosity of a shale plug is 1.76%–3.12% using current measurement methods, the upper end of which is too large to be acceptable. It is suggested that the measurement uncertainty should be factored into the standard helium gas injection porosity determination experiment, and the uncertainty should be less than 2.00% when using a high‐precision pressure gauge; (2) in order to reduce the risk for exploration and decision‐making, attention should be paid to the large uncertainty (30% at least) of shale gas resource assessment results, sometimes with corrections being made based on the practical considerations;(3) a pressure gauge with an accuracy of 0.25% of the full scal cannot meet the requirements of porosity measurement, and a high‐precision plug cutting method or high‐precision bulk volume measurement method such as one using 3D scanning, is recommended to effectively reduce porosity uncertainty; (4) the method and process for evaluating the measurement uncertainty of gas shale porosity could also be referred for assessment of experimental quality by other laboratories.

Stochastic Elasto-Plastic Analysis of Necking Bar with Random Tvergaard Coefficients

This paper reports on the computational modelling of static extension tests of the round steel bar. The main objective was to apply the generalised stochastic perturbation technique implemented as the Stochastic Finite Element Method to carry out the numerical simulation of its elasto-plastic behaviour. This approach was based on: the general order Taylor expansion of all input random variables and the resulting state functions of their average means, as well as on the Least Squares Method employed to determine analytical functions of in-between design parameters and the given structural responses. Tvergaard coefficients were assumed as the uncorrelated Gaussian random variables to check the effect of material porosity uncertainty on the statistical scattering of its deformations and stresses. The computational implementation employed the FEM system ABAQUS and computer algebra system MAPLE, including polynomial and non-polynomial local response functions of the displacements, plastic strains and reduced stresses. Moreover, 4-node axisymmetric, continuum, reduced-integration FEM elements (CAX4R) were used in the conducted analyses. The basic probabilistic characteristics of the structural response (expectations, coefficients of variation, skewness and kurtosis) were determined throughout the entire deformation process as the functions of input uncertainty level. The obtained results were finally contrasted with the classical Monte-Carlo Simulation scheme and the semi-analytical technique for input coefficient of variation of porous plasticity coefficients not larger than 0.20.

Uncertainty assessment of porosity and permeability by clustering algorithm and fuzzy arithmetic

A hybrid clustering-fuzzy arithmetic algorithm is here proposed, which uses cluster analysis to quantify porosity uncertainty, then the uncertainty is projected to the irreducible water saturation and permeability by the means of fuzzy arithmetic. The proposed method is applied to five wells of the carbonate Sarvak Formation, in an Iranian onshore oil-field. First, cluster analysis is applied to the porosity logs including neutron porosity, bulk density and sonic transit time. The uncertainty range of porosity is defined by the range of neutron porosity in each cluster. In order to estimate the core porosity, neutron porosity is calibrated to the core porosity in each cluster. Due to the average of error, the calibrated clustering-based porosity is at least 33% more accurate than the conventional methods. Based on the generalization ability of porosity estimators, a homogeneous porosity zone is determined northward. Irreducible water saturation, analyzed by the proposed method, has less overestimation, compared to the conventional evaluation of irreducible water saturation. Permeability fuzzy number is compatible with core tests, except in well S1, which is drilled in a location, compressed by two stress regimes (N-S and NW-SE). Two criteria are defined for validating the fuzzy numbers by core data: (i) Crit1 finds an average α-cut of core values; (ii) Crit2 finds the best α-cut to optimize uncertainty interval of the fuzzy number. The α > 0.90 is the most appropriate for porosity and permeability studies.

Perspectives on geochemical proxies: The impact of model and parameter selection on the quantification of carbonate recrystallization rates

Diagenetic reactions in marine sediments, such as the recrystallization of carbonates, can impact the accuracy of paleo-environmental and paleo-climatic reconstructions by geochemical proxies. The extent to which the recrystallization of carbonates affects the chemistry of sedimentary archives depends on the reaction rate, extent of isotopic disequilibrium, and duration of reaction. The reaction rate, which is obviously critical, can be constrained by the elemental and isotopic compositions of pore fluids. Such constraints are affected by assumptions regarding the temperature in the sedimentary column relative to the temperature of formation, the burial rate, pore fluid advection, the composition of the sediments (carbonate-rich versus siliciclastic), and the porosity of the sediment column. In this study, we use a steady-state analytical solution to the diagenetic equations to constrain depth-dependent reaction rates (and extents of recrystallization) based on the Ca isotopic compositions of pore fluids in sedimentary columns at multiple ocean drilling sites (Sites 807, 984, 1170, and 1171), which encompass a diverse range of sedimentary compositions and conditions. We find that carbonates in siliciclastic sediments are generally less altered by diagenesis than their carbonate-rich counterparts. The discrepancy in recrystallization rates between siliciclastic and carbonate-rich sedimentary sections is, however, significantly smaller than previously estimated, suggesting that siliciclastic archives are not immune to diagenetic effects. While we find that diagenesis can decouple contemporaneous proxies of sea surface temperature (Mg/Ca and δ18O), our calculations also reveal that δ18O-based temperature estimates are more robust in siliciclastic sections relative to carbonate-rich sections. Sensitivity tests of the calculated extent of recrystallization suggest that uncertainties in porosity and burial rate are generally the greatest sources of error to proxy reconstruction from diagenetically altered sediments. The conclusions drawn using the analytical solution are benchmarked against a depth-dependent, forward numerical model using the CrunchFlow software (Steefel et al., 2015); ultimately, this comparison demonstrates that the assumptions necessary in deriving the analytical solutions have a relatively minor impact on the resulting conclusions.

A framework for the probabilistic analysis of PEMFC performance based on multi-physical modelling, stochastic method, and design of numerical experiments

In this article, a probabilistic approach is applied to evaluate the impact of the GDL porosity uncertainty on the electrical performances of a PEMFC. The study is based on the use of a dynamical, symbolic, and acausal knowledge model. Some statistical distributions are introduced on the input model parameter (porosity) and the statistical distributions induced on the output parameters (cell voltage, resistance) are analyzed. The difference observed between the shapes of the input and output distributions (respectively some Gaussian and inverse Gamma distributions with a threshold phenomenon) is the result of strong nonlinearities linked with the integration of multiphase flow phenomena in the modelling (e.g. diffusion limit of the humidified air in the GDL). The study is also conducted for different conditions of temperature and pressure through a design of numerical experiments. One of the results obtained is that the variation coefficient related to the GDL porosity has, compared to the other parameters with their intervals of variation considered, little effect on the average output distributions. However, the dispersion introduced on the porosity impacts their shapes (e.g. significant effect on the standard deviation).

Assessment of NPV Uncertainty on Heterogeneous Reservoirs during Polymer Flood

This study shows net present value (NPV) distribution by considering uncertainties in porosity, oil viscosity, water saturation, and permeability for polymer flood with Monte Carlo simulation. For high and low average permeability conditions, differences of NPV between polymer flooding and water flooding have been investigated. According to results both average NPV and range of NPV distribution tend to increase with porosity and permeability in all cases. Although water saturation and oil viscosity affect NPV, they are not important parameters that conclude uncertainty of NPV under the conditions considered in this study. For high permeability model which has Dykstra-Parsons coefficient (DP) as 0.72 and porosity as 0.3088, Monte Carol simulations for polymer flood show that 50th percentile (P50) of NPV is 352.81 M$. If porosity is decreased from 0.3088 to 0.1912, the P50 is also decreased 63.8 %. The reduction of NPV during polymer flooding in low permeability reservoirs are almost 40 % higher than that of water flood. These differences come from polymer adsorption and permeability reduction that easily occurs in low permeability zone. The procedure has proven to be useful tool to generate probability distribution of NPV when polymer flood is selected as a tertiary flood process.

Special Section on Mass Transport and Environmental Risk Assessment

Special Section on Mass Transport and Environmental Risk Assessment

The Uncertainty Analysis of the Key Factors That Affect the AVO Attributes in Sandstone Reservoir

AbstractTraditional seismic AVO forward study usually uses constant parameters to construct rock physics models, and such parameters in real formations have uncertainties in an exploration area. This work employed laboratory measurements of cores from the target formation to simplify the rock physics model with relationships of reservoir sandstone porosities and P‐ and S‐wave impedances under a dry condition. To study influences of model parameter uncertainties, the probability density functions of key model parameters were introduced based on core measurements and well logging analysis. Using the Monte‐Carlo method and Gassmann fluid replacement technique, the AVO responses of models were obtained for saturation fluids as brine, oil and gas. The comparative analysis indicates that the velocity uncertainty of covering mud is the primary factor affecting the fluid cluster's distribution and deviation from water background trend on AVO intercept‐gradient crossplots, while the porosity uncertainty of sand is also significantly affecting but less than covering mud. Thus, the correct interpretation of AVO anomalies for real seismic data requires forward simulation on a probabilistic rock physics model to acquire the information on reservoir physical properties and fluids as much as possible.