Sequential Gaussian Simulation 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.

Geometallurgical Simulation of the Work Index in a Porphyry Copper Deposit Using Geostatistical Techniques

The spatial variability in the geometallurgical attributes of the deposits is a crucial parameter from the exploration stage, which conditions and influences the mineral processing. Consequently, the objective of this research is to elaborate the geometallurgical simulation of the Bond Work Index for a porphyry copper deposit. For this purpose, information of primary and response attributes corresponding to ore zones, lithologies and BWi contained in 1,449 samples of exploratory drill holes were used. An exploratory data analysis of this information was carried out, and geometallurgical units were defined based on the geological and processing knowledge that validates the behavior of each one of them within the deposit; then Sequential Gaussian Simulation was applied, running 100 realizations in each GMU, those that best reproduce the statistics of the original samples were chosen. The results show that the lithology of the deposit controls the BWi variability and according to the rock competence the ore zones are classified from the softest to the hardest in oxides, mixed and sulfides.

Sequential Gaussian simulation predicts soil liquefaction potential

The Yellow River Delta is covered with a large number of pipelines, but due to the complex soil composition in the region, ensuring that pipelines are not damaged by soil liquefaction is an important issue at present. Based on the simplified method of the cone penetration test (CPT), the sequential Gaussian simulation (SGS) can probabilistically simulate the liquefaction potential index (LPI) in the study area to solve the problem of the smoothing effect occurring in the kriging method. In this study, 10 experiments were conducted in the Yellow River Delta to evaluate soil liquefaction within the site using uncertainty analysis by the SGS method. The results indicate that (1) All LPI values in the study area are less than 5, with an overall sub-moderate liquefaction potential. (2) The results of the variogram model show that the Gaussian function model has the best fit with a Root Mean Squared Error of 0.429. The results of the e-type simulation realizations illustrate that the soils around the three sites S1, S5, and S10 exhibit high LPI values, distributed in a band in the middle of the western and eastern parts of the site. (3) Uncertainty analysis was performed using LPI = 2 as a threshold to explore the distribution of areas of moderate liquefaction potential and areas of low liquefaction potential in the study area. (4) Improvements were made to address the current problem of inappropriate values of liquefaction thresholds and the lack of medium liquefaction potential thresholds by proposing when LPI = 20 as the liquefaction threshold, LPI = 10 and 16 as the thresholds for low liquefaction potential, medium liquefaction potential and high liquefaction potential.

Stream sediment pollution: a compositional baseline assessment

AbstractA high concentration of potentially toxic elements (PTEs) can affect ecosystem health in many ways. It is therefore essential that spatial trends in pollutants are assessed and monitored. Two questions must be addressed when quantifying pollution: how to define a non-polluted sample and how to reduce the problem’s dimensionality. A geochemical dataset is a composition of variables (chemical elements), where the components represent the relative importance of each part of the whole. Therefore, to comply with the compositional constraints, a compositional approach was used. A novel compositional pollution indicator (CPI) based on compositional data (CoDa) principles such as the properties of sparsity and simplicity was computed. A dataset of 12 chemical elements in 33 stream-sediment samples were collected from depths of 0–10 cm in a grid of 1 km × 1 km and analyzed. Maximum concentrations of 3.8% Pb, 750 µg g−1 As, and 340 µg g–1 Hg were obtained near the mine tailings. The methodological approach involved geological background selection in terms of a trimmed subsample that could be assumed to contain only non-pollutants (Al and Fe) and the selection of a list of pollutants (As, Zn, Pb, and Hg) based on expert knowledge criteria and previous studies. Finally, a stochastic sequential Gaussian simulation of the new CPI was performed. The results of the hundred simulations performed were summarized through the mean image map and maps of the probability of exceeding a given statistical threshold, allowing the characterization of the spatial distribution and the associated variability of the CPI. A high risk of contamination along the Grândola River was observed. As the main economic activities in this area are agricultural and involve animal stocks, it is crucial to establish two lines of intervention: the installation of a surveillance network for continuous control in all areas and the definition of mitigation actions for the northern area with high levels of contamination. Graphical Abstract

Soil CO2 fluxes measured in the Acoculco Geothermal System, Mexico: Baseline emissions from a long-term prospection programme

The Acoculco Caldera Complex is considered a promissory hidden high-temperature geothermal system in Mexico. To support the geothermal prospection of this anomalous area, a comprehensive programme of soil CO2 flux measurements was performed. A long-term measurement programme was conducted to determine the baseline of natural soil CO2 effluxes. Significant efforts were devoted both to measuring the CO2 fluxes between 2015 and 2022 and interpreting their origin. Eighteen soil gas surveys of CO2 were carried out by using the accumulation chamber method. >1200 diffuse CO2 fluxes were measured in six different areas of the Acoculco Caldera. Two areas (Los Azufres and Alcaparrosa) exhibited cold degassing sites, acid-sulphate springs, and gas bubbling in surface water bodies. The soil CO2 fluxes ranged from 1 to 26,000 g m−2 d−1, whereas lower fluxes <29 g m−2 d−1 were determined as the degassing baseline. A total CO2 output of 492 t d−1 km−1 was estimated using an integrated SGS-GSA approach, where the highest total soil CO2 fluxes were obtained for Alcaparrosa (299 t d−1 km−2) compared with Los Azufres (164 t d−1 km−2), and Surroundings (29 t d−1 km−2). Such results agree well with those values measured in other worldwide volcanic and active geothermal ecosystems. The range of CO2 isotopic composition values from −28.83 ‰ to −3.11 ‰, together with their statistical distribution, suggests multiple CO2 production sources feeding soil degassing. The combined interpretation of flux and isotopic data allowed us to identify two distinct gas sources: endogenous and biogenic. The present study highlights the importance of using soil CO2 monitoring to determine baseline emissions at the early exploration stage of geothermal systems.

Iran's comprehensive heat flow map generated by the Random Forest method and the Sequential Gaussian Simulation

The objective of this research is to present a comprehensive heat flow map for Iran by utilizing a novel approach that combines geostatistical distributions, machine learning algorithms, and deterministic calculations. In order to enhance the accuracy and quality of the initial geothermal map obtained from a recent study, a machine learning technique called the Random Forest method was employed in the mapping process, taking into consideration approximately 100 processed data points. To accomplish this, various geological features such as active faults, hot springs, geological domains, and hot spots were identified as effective attributes for generating a geology-based geothermal map. This geology-based map was then used as a trend to create a geostatistics-based geothermal gradient map. The sequential Gaussian co-simulation method was adopted for the geostatistical distribution, enabling the extension of the available geothermal gradient data points (which were calculated in a recent study) to cover the entire surface of Iran on a map. Consequently, an integrated geothermal gradient map was derived. Afterwards, thermal conductivity values corresponding to the prevalent lithology of each location in Iran were incorporated to calculate the heat flow values. This process resulted in the generation of a comprehensive heat flow map. The introduction of such an extensive map would greatly contribute to future geothermal-related explorations and complementing research in this field.

Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications

For complex and multi-layered clastic oil reservoir formations, modeling lithofacies and petrophysical parameters is essential for reservoir characterization, history matching, and uncertainty quantification. This study introduces a real oilfield case study that conducted high-resolution geostatistical modeling of 3D lithofacies and petrophysical properties for rapid and reliable history matching of the Luhais oil reservoir in southern Iraq. For capturing the reservoir's tidal depositional setting using data collected from 47 wells, the lithofacies distribution (sand, shaly sand, and shale) of a 3D geomodel was constructed using sequential indicator simulation (SISIM). Based on the lithofacies modeling results, 50 sets of porosity and permeability distributions were generated using sequential Gaussian simulation (SGSIM) to provide insight into the spatial geological uncertainty and stochastic history matching. For each rock type, distinct variograms were created in the 0° azimuth direction, representing the shoreface line. The standard deviation between every pair of spatial realizations justified the number of variograms employed. An upscaled version of the geomodel, incorporating the lithofacies, permeability, and porosity, was used to construct a reservoir-flow model capable of providing rapid, accurate, and reliable production history matching, including well and field production rates.

Oil volumetric calculations of Kujung Formation in the North East Java Basin based on 3D static modelling using geostatistical approach and additional control variable

Carbonate rock is known as the leading oil and gas reservoir such as Kujung Formation in the North East Java Basin which has been proven to produce fossil energy since 2002. The extended time of oil and gas production needs to be reconsidered by investigating reservoir conditions. The 3D static modelling is constructed by integrating seismic, well log, and geological data using the geostatistical approach to delineate the petrophysical properties which can represent the reservoir heterogeneity. Oil and gas volumetric are calculated by two different techniques. First, the Sequential Gaussian Simulation (SGS) algorithm is used to distribute petrophysical properties, in particular porosity and water saturation with seismic and well log as controlling data. Similar to SGS algorithm, the other method involves AI Cube in co-kriging operation as a secondary variable for generating the porosity ratio. The oil and gas volume estimation using 3D static modelling has been done by integrating co-kriging operation and SGS algorithm establishes higher volumetric value than calculation merely involve SGS algorithm. The higher volumetric value from the combination method is more reliable due to the calculation having various variables that influence the result. The outcome of this research is perchance to beused as field development reference.

3D reservoir characterization of the Mangahewa Formation, Mangahewa Field, Taranaki Basin, New Zealand

The Mangahewa Formation is the primary reservoir target in the Mangahewa Field in the Taranaki Basin, New Zealand. This formation is distinguished by its marginal marine substantial tight-sand reservoir, having thickness exceeding 800 m. The aim of this study is to assess the reservoir properties of the Mangahewa Formation through 3D reservoir modeling, employing 3D seismic data, core data, and well data from the Mangahewa Field. Utilizing variance attributes, the faults and horizons have been identified successfully within the field. The majority of the interpreted faults exhibit dip angles exceeding 60°, with a maximum displacement of 118 m. To detect direct hydrocarbon indicators, root-mean-square amplitude seismic attribute, envelope, and generalized spectral decomposition techniques have been employed. Subsequently, four lithofacies, comprising 78.3% sandstone, 9.2% siltstone, 9.5% claystone, and 3.0% coal have been established by utilizing the Sequential Indicator Simulation (SIS) algorithm to create a lithofacies model. A property model has been generated using the Sequential Gaussian Simulation (SGS) algorithm. Petrophysical evaluation indicates that the Mangahewa Formation exhibits reservoir qualities ranging from fair to good, with porosity levels between 8% and 11%, permeability averaging up to 10 mD, variable shale volumes, and hydrocarbon saturation in the range of 40%–50%. This study's methodologies and findings can serve as a valuable foundation for similar investigations in other tight-sand gas fields located in different regions.

An improved stochastic inversion method for 3D elastic impedance under the prior constraints of random medium parameters

Three-dimensional (3D) elastic impedance inversion is widely used in oil and gas exploration with its excellent identification of reservoirs and noise immunity. However, modifying the prior information directly in inversion will undoubtedly destroy the spatial autocorrelation structure of the elastic impedance. Maintaining the elastic impedance spatial autocorrelation structure in seismic inversion is of great significance for guiding the reservoir identification and prediction. The 3D prior information construction method is proposed under the constraint of random medium parameters. This method establishes an intrinsic connection between random medium parameters and elastic impedance based on seismic data and well data, which provides a theoretical basis for simulating prior information satisfying the spatial correlation of the subsurface medium. To ensure the invariance of the spatial structure term in the inversion process, the random term and the spatial structure term in the simulation process are separated by the gradual deformation method (GDM), and the priori constrained objective function is further constructed to reduce the inversion uncertainty. Numerical example shows that the relative error of the proposed simulation method is smaller than that of conventional sequential Gaussian simulation, and the details are more accurately depicted based on the priori inversion results in this study. In addition, the elastic impedance equation based on the p-wave modulus is derived by combining with the reservoir sensitive parameters in field data. The field examples document that this method can effectively perform high-resolution geophysical prediction of underground thin-layer reservoirs.

The effect of depositional environment on sustaining a carbon storage site

One of the most difficult challenges facing planet earth is climate change caused by an increase in the concentration of greenhouse gases. Increased effort is being placed on delineating and characterizing suitable geologic formations that can house these gases. As part of this effort, we studied how the depositional environment would influence the storage of CO2 in the Illinois Basin. Lithofacies of the Mt. Simon Formation within the Illinois Basin were interpreted from the core data available for one of the four wells. The lithofacies interpreted from the core data were correlated with the motifs of the gamma-ray (GR) log in order to synchronize the information. Based on the lithofacies from the core and GR log, the depositional environment was defined. This was further used to establish a stratigraphic correlation across the four wells. Petrophysical analysis was carried out to evaluate the suitability of the rock properties in storing CO2. Structural features such as faults were interpreted on the 3D seismic data to evaluate the sealing capacity of the Mt. Simon Formation. A conceptual model of the depositional environment was developed and integrated with the rock properties using sequential Gaussian simulation. CO2 gas was injected and simulated in the Mt. Simon geologic model to evaluate how the flow would be impacted by the formation's depositional environment over time. Based on the lithofacies interpreted from the geologic core data and GR log, four depositional environments were interpreted — a braided river system, a fluvial deposit, a flood plain/shallow ephemeral environment, and eolian deposits. The structural framework revealed that the lower section of the Mt. Simon Formation is laterally extensive and structurally controlled. This allows CO2 gas to be evenly distributed throughout the formation.

Improving assessment quality of soil natural attenuation capacity at the point and regional scales.

Soil natural attenuation capacity (NAC) is an important ecosystem service that maintains a clean environment for organisms in the soil, which in turn supports other services. However, spatially varying indicator weights were rarely considered in the traditionally-used soil NAC assessment model (e.g., ecosystem-service performance model) at the point scale. Moreover, in the spatial simulation of soil NAC, the traditionally-used geostatistical models were usually susceptible to spatial outliers and ignored valuable auxiliary information (e.g., land-use types). This study first proposed a novel soil NAC assessment method based on the ecosystem-service performance model and moving window-entropy weight method (MW-EW) (NACMW-EW). Next, NACMW-EW was used to assess soil NAC in a typical area in Guixi City, China, and further compared with the traditionally-used NACtra and NACEW. Then, robust sequential Gaussian simulation with land-use types (RSGS-LU) was established for the spatial simulation of NACMW-EW and compared with the traditionally-used SGS, SGS-LU, and RSGS. Last, soil NAC's spatial uncertainty was evaluated based on the 1000 realizations generated by RSGS-LU. The results showed that: (i) MW-EW effectively revealed the spatially varying indicator weights but EW couldn't; (ii) NACMW-EW obtained more reasonable results than NACtra and NACEW; (iii) RSGS-LU (RMSE = 0.118) generated higher spatial simulation accuracy than SGS-LU (RMSE = 0.123), RSGS (RMSE = 0.132), and SGS (RMSE = 0.135); and (iv) the relatively high (P[NACMW-EW(u) > 0.57] ≥ 0.95) and low (P[NACMW-EW(u) > 0.57] ≤ 0.05) threshold-exceeding probability areas were mainly located in the south and east of the study area, respectively. It is concluded that the proposed methods were effective tools for soil NAC assessment at the point and regional scales, and the results provided accurate spatial decision support for soil ecosystem service management.

Perspectives of 3D Probabilistic Subsoil Modeling for BIM

Building information modeling (BIM) in the planning and construction of infrastructure projects, such as roads, tunnels, and excavations, requires the generation of comprehensive 3D subsoil models that encompass relevant geological and geotechnical information. Presently, this process relies on the deterministic interpolation of discrete data points obtained from exploratory boreholes and soundings, resulting in a single deterministic prediction. Commonly employed interpolation methods for this purpose include radial basis function and kriging. This contribution introduces probabilistic methods for quantifying prediction uncertainty. The proposed modeling approach is illustrated using simple examples, demonstrating how to use sequential Gaussian and Indicator Simulation techniques to model sedimentary processes such as erosion and lenticular bedding. Subsequently, a site in Munich serves as a case study. The widely used industry foundation classes (IFC) schema allows the integration of the model into the BIM environment. A mapping procedure allows transferring voxel models to the IFC schema. This article discusses the significance of incorporating uncertainty quantification into subsoil modeling and shows its integration into the BIM framework. The proposed approach and its efficient integration with evolving BIM standards and methodologies provides valuable insights for the planning and construction of infrastructure projects.

Spatial prediction of soil sand content at various sampling density based on geostatistical and machine learning algorithms in plain areas

Accurate prediction of the spatial distribution of soil sand content is a pre-requisite for land use management, soil quality evaluation and erosion control, as it determines the transport and movement of soil water, fertilizer, air and heat. Digital soil mapping (DSM) is extensively employed for predicting soil properties. However, practical research is required to address the challenge of selecting an optimal prediction model that is both cost-effective and accurate at a specific sampling density. In this study, topsoil samples were collected from 2,848 sampling points in the eastern plains of China (107,200 km2). The performance of different prediction models for mapping soil sand content was compared at 12 levels of sampling density. Moreover, the geographical detector, a statistical method used to assess the spatial stratified heterogeneity of variables, was adopted to determine the major drivers of spatial variation in soil sand content. The results indicated that climate factors are the major drivers of the spatial variability in soil sand content. For the 100% sample size (26.57 samples/103 km2), the geostatistical models that did not depend on environmental variables (ordinary kriging, sequential Gaussian simulation) performed best, followed by the machine learning models (random forest, cubist and support vector machine) and the geostatistical model with environmental variables (co-kriging). Sampling density had a considerable impact on model accuracy, and the advantages of machine learning models became apparent when sampling densities were below 20% (5.31 samples/103 km2). Therefore, the best combination of prediction model and sampling density should be selected to obtain maps of soil sand content economically and accurately. This study provides a valuable reference for the selection of prediction methods in the practical application of DSM.

Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation

The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest biomass, achieving a more efficient and effective monitoring system. In this paper, we used Sequential Gaussian Conditional Simulation (SGCS) to obtain the biomass of four typical forest types in Shangri-La, Yunnan Province, China. In addition, we adopted a geostatistical sampling method for sample point layout and optimization to achieve the purpose of improving sampling efficiency and accuracy, and compared with the traditional sampling method. The main results showed that (1) the Gaussian model, exponential model, and spherical model were used to analyze the variogram of the four typical forests biomass, among which the exponential model had the best fitting effect (R2 = 0.571, RSS = 0.019). The range of the exponential model was 8700 m, and the nugget coefficient (C0/(C0 + C)) was 11.67%, which showed that the exponential model could be used to analyze the variogram of forest biomass. (2) The coefficient of variation (CV) based on 323 biomass field plots was 0.706, and the CV based on SGCS was 0.366. In addition, the Overall Estimate Consistency (OEC) of the simulation result was 0.871, which can be used for comparative analysis of traditional and spatial sampling. (3) Based on the result of SGCS, with 95% reliability, the sample size of traditional equidistant sampling (ES) was 191, and the sampling accuracy was 95.16%. But, the spatial sampling method based on the variation scale needed 92 samples, and the sampling accuracy was 93.12%. On the premise of satisfying sampling accuracy, spatial sampling efficiency was better than traditional ES. (4) The accuracy of stratified sampling (SS) of four typical forest areas based on 191 samples was 97.46%. However, the sampling accuracy of the biomass variance stratified space based on the SGCS was 93.89%, and the sample size was 52. Under the premise of satisfying the sampling accuracy, the sampling efficiency was obviously better than the traditional SS. Therefore, we can obtain the conclusion that the spatial sampling method is superior to the traditional sampling method, as it can reduce sampling costs and solve the problem of sample redundancy in traditional sampling, improving the sampling efficiency and accuracy, which can be used for sampling estimation of forest biomass.

Geostatistics Application in 3-D Reservoir Modeling for Calculation of Hydrocarbon Resources, Case Study: North Rembang Area

Oil and gas are still indispensable commodities to meet Indonesia's energy needs in the next few decades, even though government policies related to energy transition towards net zero emission have begun to be encouraged. However, the oil and gas requirement is not followed by the availability of reserves or resources owned by Indonesia. The objective of this study is to discover new hydrocarbon resources, through 3-D reservoir modeling using geostatistical method. The geostatistical method is still the best current method to predict the distribution of facies association and reservoir petrophysical properties such as porosity and water saturation. In facies association modeling, a stochastic geostatistical method is used, namely the Truncated Gaussian with Trends (TGS) algorithm. For porosity and water saturation (SW) modeling, the Sequential Gaussian Simulation (SGS) algorithm was applied. The results of 3-D modeling of facies associations, porosity, and water saturation are utilized to estimate gas resources in the North Rembang Area. From the estimation of gas resources, it is recognized that the Rembang Utra Area has accumulated gas resources around 3 TCF.

Integration of rock physics, geostatistics, and Bayesian algorithm to estimate porosity in tight oil reservoirs

The conglomerate reservoirs in the Triassic Baikouquan Formation have been identified as the major target for hydrocarbon exploitation in the Mahu Sag, Junggar Basin, Western China. Nevertheless, conglomerate reservoirs typically have lower porosity than sandstone reservoirs. Therefore, determining sweet spots is critical in reservoir modeling and further hydrocarbon exploitation. To investigate favorable areas and better predict the porosity of the Baikouquan tight oil reservoir, integrated methods were employed, including casting thin sections, scanning electron microscopy (SEM), wireline logs, rock physics template (RPT) modeling, geostatistical simulation, and Bayesian updating. The results showed that pore types consist of microfractures, intercrystal micropores, residual intergranular pores, and dissolution pores. Four rock types (RT) were identified via the built RPT, wherein velocity and porosity had a nonlinear relationship. Afterward, porosity simulation was performed and compared using sequential gaussian simulation (SGS), SGS with collocated cokriging (SGSC), and SGS with Bayesian updating (SGSB). Prior to the simulation, all the data were preprocessed to follow a Gaussian (normal) distribution. The range and azimuth of different RT in variogram modeling were then determined using data from horizontal wells. Consequently, it was revealed that the probability distribution frequency (PDF) of SGS with Bayesian updating was very similar to the hard data, whereas the PDF associated with SGS and SGSC may be biased. Particularly the PDF was too narrow for high values. The cumulative distribution function (CDF) of SGSB also showed a similar trend to the CDF of the input data. The ensemble-averaged (E-type) maps and quantiles maps revealed that SGSB had an advantage over collocated cokriging because it could capture the non-linear relationships between impedance and porosity. This study indicates that Bayesian updating is a reliable technique for incorporating secondary data in the calculation of reservoir properties.

The Best Scenario for Geostatistical Modeling of Porosity in the Sarvak Reservoir in an Iranian Oil Field, Using Electrofacies, Seismic Facies, and Seismic Attributes

Summary The lateral and vertical variations in porosity significantly impact the reservoir quality and the volumetric calculations in heterogeneous reservoirs. With a case study from Iran’s Zagros Basin Sarvak reservoir in the Dezful Embayment, this paper aims to demonstrate an efficient methodology for distributing porosity. Four facies models (based on electrofacies analysis data and seismic facies) with different geostatistical algorithms were used to examine the effect of different facies types on porosity propagation. Both deterministic and stochastic methods are adopted to check the impact of geostatistical algorithms on porosity modeling in the static model. A total of 40 scenarios were run and validated for porosity distribution through a blind test procedure to check the reliability of the models. The study’s findings revealed high correlation values in the blind test data for all porosity realizations linked to seismic facies, ranging from 0.778 to 0.876. In addition, co-kriging to acoustic impedance (AI), as a secondary variable, increases the correlation coefficient in all related cases. Unlike deterministic algorithms, using stochastic methods reduces the uncertainty and causes the porosity model to have an identical histogram compared with the original data. This study introduced a comprehensive workflow for porosity distribution in the studied carbonate Sarvak reservoir, considering the electrofacies, and seismic facies, and applying different geostatistical algorithms. As a result, based on this workflow, simultaneously linking the porosity distribution to seismic facies, co-kriging to AI, and applying the sequential Gaussian simulation (SGS) algorithm result in the best spatial modeling of porosity.

Effects of Reservoir Heterogeneity on CO2 Dissolution Efficiency in Randomly Multilayered Formations

Carbon dioxide (CO2) dissolution is the secondary trapping mechanism enhancing the long-term security of CO2 in confined geological formations. CO2 injected into a randomly multilayered formation will preferentially migrate along high permeability layers, increasing CO2 dissolution efficiency. In this study, sequential Gaussian simulation is adopted to construct the stratified saline formations, and two-phase flow based on MRST is established to illustrate the spatial mobility and distribution of CO2 migration. The results show that gravity index G and permeability heterogeneity σY2 are the two predominant factors controlling the spatial mobility and distribution of CO2 transports. The CO2 migration shows a totally different spatial mobility under different gravity index and heterogeneity. When the permeability discrepancy is relatively larger, CO2 preferentially migrates along the horizontal layer without accompanying the vertical migration. For the formation controlled by gravity index, CO2 migration is governed by supercritical gaseous characteristics. For the medium gravity index, the upward and lateral flow characteristics of the CO2 plume is determined by gravity index and heterogeneity. When the gravity index is smaller, permeability heterogeneity is the key factor influencing CO2 plume characteristics. Permeability heterogeneity is the decisive factor in determining final CO2 dissolution efficiency. This investigation of CO2 mobility in randomly multilayered reservoirs provides an effective reference for CO2 storage.

Combined synchronous simulation of discrete and continuous variables under high net-to-gross ratio reservoir

The Triassic TIII oil group in the Sangtamu oil field in the Tarim Basin is formed by multiple phases of channels cutting and superimposing one another, and the net-to-gross (NTG) ratio can exceed 90%. Strong heterogeneity exists in the spatial storage and seepage features of the sand body within the channel, causing significant uncertainty in the 3D geological modeling of the reservoir. In order to delicately characterize the reservoir heterogeneity within the channel in its various phases, this paper proposes a facies modeling method based on the deposition process. It includes a strategy of joint synchronous simulation of discrete facies and continuous physical property variables, fused trend body-constrained modeling established by the internal variation characteristics of single-phase channel sand, and an increase in channel phases to meet high NTG ratio reservoir conditions. In this process, the object-based method and sequential gaussian simulation method are both used to simulate step-by-step the 3D geological model of the facies and physical properties of different phase channels, respectively. The results show that 61 small sand bodies compose the results of the established 3D geological model, and the physical simulation results reflect the characteristics of physical changes inside the channel at different phases. The model has been verified by vacuate wells validation with a compliance rate of 97.4%, and the method reduces the model's multiple solutions and uncertainties while offering a new practical method for modeling complex fluvial facies reservoirs.