Sequential Gaussian Simulation Research Articles

Quantification and validation of uncertainties in subsoil models

AbstractIn infrastructure planning and construction, modeling the subsoil and its associated uncertainty is a fundamental task of geotechnical engineers. However, probabilistic methods and tools for quantifying and displaying the uncertainty of the subsoil models are rarely used in practice where deterministic interpolation dominates. In digital planning using Building Information Modeling (BIM), the probabilistic approach supports creating a discipline model in which the uncertainties of the spatial layer structure are statistically quantified to evaluate the georisks in the design and execution of civil constructions. This article presents a case study using a combination of Sequential Gaussian Simulation (SGSIM) and Sequential Indicator Simulation (SISIM) to account for uncertainties in soil layer geometry. In a case study at the Munich Town Hall, a geostatistical approach is applied and validated based on 70 bore logs, whereby the probabilities for the occurrence of a particular layer are spatially quantified. The case study illustrates the methodology‘s great potential and benefits compared to the conventional deterministic approach based on interpolation procedures.

Collaborative Seismic Impedance Inversion under Bayesian Parameter Estimation

Abstract At present, the industry mainly divides seismic inversion methods into deterministic inversion and deterministic inversion based on the methods and principles used in inversion. There are two types of geostatistical inversion. Deterministic inversion is the process of directly modifying the model quantity through optimization algorithms under given initial constraint conditions to obtain a definite solution; Geostatistical inversion uses random simulation methods to obtain inversion results through simulation and selection. The seismic data did not participate in the direct modification of the model, but rather served as a matching optimization constraint for the random model. Starting from the Bayesian formula, this article unifies deterministic inversion and geostatistical inversion within the Bayesian parameter estimation framework. From a theoretical framework, it analyzes the similarities and differences between deterministic inversion and geostatistical inversion, and proposes a method that combines deterministic inversion and geostatistical inversion - seismic wave impedance co inversion. Sequential Gaussian simulation and model constrained inversion are used as examples of the two major methods, The correctness of the research results and the feasibility and effectiveness of the proposed new inversion method and process have been verified through the application of the Marmousi theoretical model and actual data from Fudong, Fukang Depression, Xinjiang. It has been proven that the proposed method can improve the accuracy and precision of inversion. The inversion method and process used in this study obtained high-resolution and high-precision inversion results, which have good application prospects.

CO2 storage in saline aquifers: A simulation on quantifying the impact of permeability heterogeneity

As one promising technology to mitigate CO2 emission, CO2 capture utilization and storage (CCUS) plays a crucial role in achieving carbon neutrality. The deep saline aquifer is a prime candidate for CO2 geological storage. The subsurface flow and effectiveness of CO2 storage in deep saline aquifer are highly related to the permeability heterogeneity of formations, which needs in-depth study. To bridge the gap, this study focused on quantifying the impact of permeability heterogeneity on the CO2 flow patterns, the well injectivity and the storage efficiency, which aims to enhance the understanding of CO2 storage characteristics in deep saline aquifers. The heterogeneous saline aquifer model was built by using the Sequential Gaussian Simulation (SGSIM) method. A novel classification template was proposed with the CO2 flow patterns classified as “Dispersive”, “Sweeping”, “Fingering”, and “Channeling” for CO2 storage in deep saline aquifers according to the geostatistical parameters (including dimensionless correlation lengths, first and second spatial moment, and Dykstra-Parsons coefficient). For the well injectivity, the results showed that high injectivity occurred when both horizontal and vertical correlation lengths were high or at a favourable connectivity of permeability in the horizontal and vertical directions. In addition, CO2 storage efficiency is higher when both dimensionless horizontal and vertical correlation lengths are in the range of 0.1–0.2. This is because frequent changes between high and low permeability regions led to frequent mutual displacement of water and CO2, resulting in higher residual trapping and slow CO2 front. Therefore, the dimensionless correlation lengths and breakthrough time should be considered simultaneously to achieve safer trapping and maximize the CO2 storage capacity. The findings of this work provide insights into the subsurface flow of CO2 storage in deep saline aquifers with permeability heterogeneity.

Seismic Inversion Techniques and Attribute Analysis for Accurate Well Placement in Offshore Niger Delta Basin, Nigeria

Before this study, simple methods like seismic attribute analysis have often been used for reservoir characterization with successes however, there is still the need to reduce exploration uncertainty to a negligible level and boost investors’ confidence. This study integrated seismic inversion with seismic attribute analysis to better characterize the reservoirs in MTW Field in deep-offshore Niger Delta Basin. Five (5) wells with complete suite of petrophysical logs, three-Dimensional (3-D) seismic data, checkshot and other well information were used. The well data were thoroughly quality checked, reservoirs were litho-stratigraphically delineated and used for petrophysical analysis across the wells. This was followed by seismic-to- well-tie, seismic interpretation, and seismic attribute analysis (Root Mean Square-RMS) generated using depth surface maps. 3-D static reservoir model and volumetric evaluation were carried out. Petrophysical properties were derived and distributed across the 3-D static model using sequential gaussian simulation algorithm to ascertain shale volume spread across the model. To improve the seismic resolution and reduce interpretation uncertainty at greater depth, model- based post-stack seismic inversion was performed to obtain acoustic impedance cube. Litho-stratigraphic and petrophysical analysis result revealed five reservoir sands (A, B, C, D, and E). Reservoir-A was seen to be more viable with a thickness of 13.42 m, high effective porosity of 27%, permeability of 3187.53 mD, low water saturation of 34% and low shale volume of 11% which are indication good reservoir quality and producibility. The seismic interpretation revealed thirty-one (31) growth and antithetic faults oriented in the NE-SW and NW-SE directions, respectively. The RMS result revealed high amplitude reflectivity which is a measure of zone of interest. Based on this, seven (7) prospects and three (3) leads were identified. The seismic inversion result shows a high level of accuracy with a correlation coefficient of 0.997; 0.997; 0.995; and 0.996 in MTW-001, MTW-003ST1, MTW-004ST1 and MTW-005 wells, respectively. The acoustic impedance successfully resolved and improved on the resolution of the seismic stacking velocity especially at reservoir layers and at depth deeper than 3600 ms. Acoustic impedance as a layer property has improved on the lateral and vertical resolutions of the data beyond what the usual seismic interval velocity could image thus, validating some of the prospects and leads identified in this study. This demonstrated that uncertainty can be reduced by a blend of RMS and seismic inversion in identifying reservoir for accurate placement of wells. It is therefore recommended that E and P operators should adopt the technique in their future hydrocarbon exploration endeavor in frontier and matured basins.

Evaluating Daily Water Stress Index (DWSI) Using Thermal Imaging of Neem Tree Canopies under Bare Soil and Mulching Conditions

Water stress on crops can severely disrupt crop growth and reduce yields, requiring the accurate and prompt diagnosis of crop water stress, especially in semiarid regions. Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (Tc), which is the basis of the daily water stress index (DWSI) calculation. This research aimed to evaluate the variability of plant water stress under different soil cover conditions through geostatistical techniques, using detailed thermographic images of Neem canopies in the Brazilian northeastern semiarid region. Two experimental plots were established with Neem cropped under mulch and bare soil conditions. Thermal images of the leaves were taken with a portable thermographic camera and processed using Python language and the OpenCV database. The application of the geostatistical technique enabled stress indicator mapping at the leaf scale, with the spherical and exponential models providing the best fit for both soil cover conditions. The results showed that the highest levels of water stress were observed during the months with the highest air temperatures and no rainfall, especially at the apex of the leaf and close to the central veins, due to a negative water balance. Even under extreme drought conditions, mulching reduced Neem physiological water stress, leading to lower plant water stress, associated with a higher soil moisture content and a negative skewness of temperature distribution. Regarding the mapping of the stress index, the sequential Gaussian simulation method reduced the temperature uncertainty and the variation on the leaf surface. Our findings highlight that mapping the Water Stress Index offers a robust framework to precisely detect stress for agricultural management, as well as soil cover management in semiarid regions. These findings underscore the impact of meteorological and planting conditions on leaf temperature and baseline water stress, which can be valuable for regional water resource managers in diagnosing crop water status more accurately.

Geological Modeling of Shale Oil in Member 7 of the Yanchang Formation, Heshui South Area, Ordos Basin

In recent years, the Chang 7 member of the Mesozoic Triassic Yanchang Formation in the Ordos Basin has emerged as a significant repository of abundant and distinctive unconventional oil resources. The Heshui area boasts substantial shale oil reserves, with reported third-level reserves surpassing 600 million tons. However, the region in the southern part of Heshui is marked by pronounced variability in reservoir quality, intricate oil–water dynamics, low formation energy, and suboptimal fluid properties, leading to divergent development outcomes for horizontal wells. There is an imperative need to devise and refine new geological models to underpin the efficient exploitation of shale oil in the southern Heshui area. This study focuses on the shale oil reservoir of the Chang 7 member in the southern Heshui area of the Ordos Basin, conducting detailed stratigraphic correlation and establishing a refined isochronous stratigraphic framework. Utilizing PetrelTM modeling software (version 2018), we integrate deterministic and stochastic modeling approaches, adhering to the principles of isochronous and phased modeling. By assessing the thickness of sand and mudstone layers and the overall stratigraphic sequence, we derive a geological probability surface. Subsequently, this surface is harnessed to constrain the lithofacies, yielding a constrained lithofacies model. Employing sequential indicator simulation and sequential Gaussian stochastic simulation, we develop a reservoir attribute model that is anchored in the lithofacies model and its controls, culminating in a robust and dependable static model. Employing the geological probability surface constraint method, we meticulously construct the reservoir matrix model, amalgamating individual well data with the inherent certainty and randomness of reservoir plane thickness. This approach further enhances the model’s accuracy and mitigates the uncertainty and randomness associated with inter-well interpolation to a significant degree.

Sequential Gaussian simulation for mapping the spatial variability of saturated soil hydraulic conductivity at watershed scale

Sequential Gaussian simulation for mapping the spatial variability of saturated soil hydraulic conductivity at watershed scale

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.

Prediction of Inter-Well Petrophysical Properties from Seismic Model: A Case of Egbem Field, Niger Delta, Nigeria

We have generated a seismic model for the prediction of inter-well petrophysical properties of Egbem Field; in the Niger Delta, Nigeria. Acoustic Impedances (AI) were calculated from sonic and density logs in the available 24 wells. Reflection Coefficients (RC) determined from the acoustic impedances were convolved with modelled wavelet to produce synthetic seismograms at various well locations. A top structure map of a reservoir from the field was used to define the position of faults, which served as key pillars in gridding the reservoirs of the field into geocellular blocks of 50 x 50 meters each. Petrophysical analysis of the field showed an average porosity of 24.8% and volume of shale of 19.9%. Petrophysical parameters were geostatistically distributed on the generated framework model using Sequential Gaussian Simulation (SGS). The result of the Acoustic Impedance classification reveals four lithologic facies namely, shale, sandy shale, shaley sand and sand facies. The facies model indicates high and low acoustic impedances for sand and shale respectively. This model indicates that sand which is a good reservoir is concentrated in the central part of the field. The result of synthetic seismic generation showed a very good match between acoustic impedance and lithology. Comparison of porosity obtained from the porosity model with conventional well log porosity showed similarities in their histogram distribution. Finally, Three-dimensional seismic model of Egbem Field has been constructed using log data from twenty four wells. The application of geostatistical analyses extrapolated and interpolated these log data to cover the entire field.

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.