Sequential Indicator Simulation Research Articles

Uncertainty Study Aids Development-Plan Optimization in Deepwater Gulf of Mexico

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32622, “Development-Plan Optimization and Uncertainty Study in a Major Deepwater Field in the US Gulf of Mexico,” by Mohsen Rezaveisi, SPE, Jennifer L. Campbell, and Paula L. Wigley, Woodside Energy Group, et al. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. _ This paper describes development-plan optimization and a probabilistic uncertainty study using Latin hypercube experimental design constrained to production performance in Lower Miocene (LM) reservoirs of the deepwater Gulf of Mexico Shenzi Field. The purpose of the development-plan optimization was to identify, rank, and characterize future development opportunities (i.e., infills and injectors) in the LM reservoirs to arrest field decline. The study uses history-matched dynamic simulation models. Introduction The Shenzi discovery is approximately 122 miles off the coast of Louisiana in a water column approximately 4,400 ft deep (Fig. 1). This part of the field is a partially filled three-way closure against a salt-cored anticline. The discovery includes four main sand units (A, B, C, and D) that exhibit various degrees of hydraulic connectivity based on pressure data and production history. The area of interest in the current study is the Eastern part of the field. Static Model Build The geomodel covering the area of interest was built from two seismically interpreted horizons and approximately 180 faults. The model is subdivided into 13 zones using well-based true stratigraphic thickness maps. The average cell height is 2–3.5 ft thick with heterolithics driving the lower end of the range. Sequential indicator simulation was used to populate facies using well-based net sand trend maps by zone. In zones with evidence of channelization, a second nested facies model was used to build channels into the model. Shenzi East reservoir properties are good because of the high-quality sand deposited in the field. Lower reservoirs (C and D) have slightly more depositional complexity because of the varying paleotopography and a lower average net-to-gross (NTG) of approximately 53%. Upper reservoirs (A and B) had a larger sediment supply and were more continuously deposited, which resulted in a larger average NTG of approximately 67%. Dynamic Model History Matching The Shenzi East reservoir has been on production since March 2009, providing abundant production data for history matching. Of seven producers, six are still active, and the reservoir also features three water injectors. Producers and injectors are identified with letters P and I, respectively, followed by well numbers. Well P7 was drilled after 6 months of production from other producers; formation pressure data showing reservoir depletion are available for history matching. The water injectors were drilled in 2012–2013, but only two have formation pressure testing data available. Well P1 was completed in all sands; Wells P2, P3, and P6 were completed in three sands (D, C, and B); and the rest of the producers were completed in two sands (D and C). Production is commingled in all producers, resulting in allocation uncertainties between sands. Water production has been observed in five wells, with the current water cut in the range of 35–65%. The two most updip wells, P1 and P7, are still producing at zero water cut.

Influence of sedimentary architecture on static connectivity and geothermal doublet performance (Mezőberény, SE Hungary)

Although the Pannonian Basin in Hungary boasts significant geothermal potential, most geothermal wells in the region suffer from reinjection issues. The aim of this study is to examine the impact of the sedimentary architecture of a paralic lacustrine succession on the static connectivity of a geothermal aquifer. This problem was examined for a case-study geothermal doublet drilled in Mezőberény, southeast Hungary, which experienced injectivity problems shortly after commencing operations in 2012, ultimately leading to shut-in of the wells. A characterization of the architecture of the deltaic aquifer has been undertaken through a novel workflow that combines sedimentological and sequence stratigraphic analysis of the well-doublet and stochastic 3D modelling techniques to assess the likely static connectivity between the production and injection wells. Planform dimension of aquifer-forming mouth-bar elements and fluvial channel bodies were estimated from geological analogues, selected based on the thickness values of such sedimentary bodies interpreted on the gamma-ray logs of the well doublet and nearby hydrocarbon wells. In total five mouth-bar and two fluvial channel input geometry groups were selected, and fifty object-based realizations were constructed to analyse the static connectivity of different geological scenarios. The internal lithology of mouth-bar and fluvial-channel elements were modelled by sequential indicator simulations (ten realizations). Results of connected volume analysis indicate that the sandstone content of the aquifer is located within vertically segmented fluvial-channel and mouth-bar elements separated by mud-prone prodelta and lower delta-front deposits. This architectural configuration, in combination with low net-sandstone content suggests limited connectivity between the wells. Across the set of stochastic realizations, the volume of sandstone connecting the injection and production wells is ∼4% of the total aquifer volume, on average. Results of this study shows that simplified reservoir models can lead to over-estimation of connectivity of geothermal doublets. The proposed workflow combines sedimentological analysis and 3D modelling supported by geological analogues to predict sandstone reservoir connectivity of deltaic geothermal reservoirs. The described workflow can be carried out in target areas using existing boreholes to assist borehole planning.

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.

Exploring the uncertainty of machine learning models and geostatistical mapping of rare earth element potential in Indiana coals, USA

Rare earth elements and yttrium (REEs) have a wide range of applications in high- and low-carbon technologies. The strategic significance of REEs has grown due to their expanding applications in manufacturing industries and the constrained availability of these essential resources. This research explores the applicability of machine learning models and their uncertainty for assessing the REE potential in coal beds using various coal parameters as inputs. The work focuses on developing a predictive model based on geological variables, excluding considerations related to potential shifts in the commodities market. The Indiana Coal Quality Database was used as the data source. The promising and unpromising indicators derived from the outlook coefficient of samples from the database were used as the REE potential indicator for machine learning classification models. The filter-based approach with bootstrap was used to evaluate the importance of the coal parameters and their prediction uncertainties. Four machine learning methods (linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), a data balancing and augmentation approach (Synthetic Minority Over-sampling Technique), and bootstrap resampling techniques were used for building the models and evaluating their prediction capabilities under uncertainty. It was determined that the SVM bootstrap model with ten-times balanced and augmented data provided superior results compared with other models. Finally, stochastic spatial maps of the REE potential within the coal basin were generated using sequential indicator simulation. The spatial maps of the REE potential showed that a 29% area of the Indiana section of the Illinois coal basin has economic potential of REEs, with 90% confidence.

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.

Geostatistical simulations of the spatial variability of hydraulic conductivity in an alluvial-marine sedimentary system in Beihai City, China

Sustainable aquifer management in coastal cities is becoming critically important due to the challenges of climate variability amidst population growth. Study of the aquifer heterogeneity is effective in highlighting the aquifer behavior. In this study, we took the case of Beihai to explore the heterogeneity of its subsurface system. 8629 samples from 111 boreholes were divided into four hydrofacies units as discrete geological variables according to their hydraulic conductivities (K) and used as input datasets for the models. Sequential Indicator Simulation (SISIM) and Transition Probability Geostatistical Simulation (T-PROGS) were employed to construct 3D hydrofacies stochastic models. The simulated results realistically represent horizontal river-driven sediment distribution and vertical deposition processes of K in the aquifer, where the northeastern part is mainly composed of low K and the southern part forms the main multi-layered aquifer system with progressively increasing medium and high K. With a comparative analysis on the ability of capturing geological characteristics using both SISIM and T-PROGS, it highlighted that SISIM reduces initial data requirements without the need of Gaussian transformation and is more broadly applicable, while T-PROGS prefers considering the internal correlation of hydrofacies units and is applicable in environments where lateral accumulation is prevalent. This study demonstrates the credibility of stochastic simulation methods in recreating complex costal sedimentary environments, which provide environment for further groundwater modeling and exploration. Multi-interleaved source data and more accurate algorithms will be integrated in aquifer heterogeneity as subsequent research priorities.

Applying Facies Characterization to Build 3D Rock-Type Model for Khasib Formation, Amara Oil Field

Building a rock-type (facies) model is critical in the process of constructing the geological model.The facies model utilizes the facies obtained from the different characterization methods to represent the structure and the geology of the studied reservoir. Therefore, the importance of building a 3D rock-type model is to distribute the petrophysical properties depending on the rock facies to reflect the actual distribution and achieve a realistic model.Khasib formation in Amara oil field is mainly composed of limestone and exhibits a high degree of heterogeneities in its petrophysical and geological features, showing a complicated porosity-permeability relationship. Hence, the formation should be characterized by facies with specific property relationships and use these facies to build an accurate rock-type model to represent the geological rock modeling for this carbonate formation.The Rock-Type model for Khasib formation has been constructed using the facies obtained from flow zone indicator and cluster analysis characterization methods, these facies were distributed with the PETREL software by applying the sequential indicator simulation (SIS) algorithm.The 3D Rock- type model establishes that a different rock type characterized each unit within Khasib formation; the reservoir units (KH11, KH12, and KH2) were characterized by the presence of RT-1, RT-2,andRT-3, which represent the best, good, and intermediate reservoir quality rocks, respectively. The best rock type RT-1 with the best reservoir petrophysical properties was only noticed in unit KH2. The non-reservoir units (Khasib, Base KH11, Base KH12, and Base KH2) were characterized by RT-4 and RT-5, which reflect the bad rock quality with non-reservoir properties.

Development of Groundwater Flow Models for the Integrated Management of the Alluvial Aquifer Systems of Dravsko polje and Ptujsko polje, Slovenia

With increasing exploitation of groundwater resources and implementation of various activities in their recharge areas, it is vital to conduct a comprehensive assessment of aquifers to ensure their conservation and sustainable management. In the present study, we used a comprehensive approach to conceptualise and identify the functioning of two connected aquifer systems in north-eastern Slovenia: the Quaternary porous aquifers Dravsko polje and Ptujsko polje. The study presents the conceptual models of both aquifers and their interconnectedness using separate mathematical-numerical models with the aim of ensuring an integrated management of these alluvial aquifer systems. It also highlights the importance of understanding connections between such systems for simulating groundwater flow and transport of different contaminants. To describe the entire aquifer system, the study defines its three essential elements: the geometry of the aquifers, their recharge by precipitation, and other boundary conditions. The geometry of the Quaternary aquifers was defined using Sequential Indicator Simulation (SIS) with the ESRI’s ArcMap software. Next, LIDAR was used for determining their surface geometry. The hydrogeologic model was designed using the Groundwater Modelling System (GMS) developed by AQUAVEO. We used the MODFLOW 2000 calculation method based on the finite difference method (FDM). The model was calibrated with the PEST module, which was used to calibrate hydraulic conductivity and hydraulic heads between the measured and modelled data. Finally, the model was validated using the Nash–Sutcliffe (NSE) efficiency coefficient. In addition, the model results estimated using the PEST tool were validated with the hydraulic conductivities determined at the pumping sites (pumping tests), each belonging to water protection zones that define the maximum travel time of the particles. This was performed using the MODPATH method. The paper also presents the possibility of modelling heterogeneous but interdependent aquifers in a groundwater body. Modelling the connection between the two aquifers, which are the most important ones in the region, is essential for a comprehensive management of the entire system of water resources. The models allow for a better understanding of groundwater flow in both aquifers. Moreover, their interconnectedness will be used for further studies in this field, as well as for integrated water management.

A novel geostatistical index of uncertainty for short-term mining plan

ABSTRACT The uncertainty associated with geostatistical estimates is as important in a model as the estimated value itself. This uncertainty is commonly assessed through different scenarios obtained by geostatistical simulation methods. Although the applications of geostatistical simulation methods have grown significantly in recent decades in long-term modeling, the same improvement has not occurred in medium- and short-term modeling. This study presents the “geological-operational risk indicator,” which comprises: (1) the uncertainty of geological contacts, determined by the variance of the sequential indicator simulation; and (2) the continuity of grades, obtained by sequential Gaussian simulation for variability of grades in relation to the economic or operational limit of that variable. The proposed index is applied to five grade variables of a short-term model of the Capitão do Mato iron mine in Brazil. The results are satisfactory and advance the concept of a geological risk indicator that is appropriate for medium- and short-term operations. This novel indicator helps to predict and manage in advance the uncertainties associated with mining plans.

3-D tight sandstone gas outcrop simulation based on unmanned aerial vehicle oblique photography data— A case study from the Pingtouxiang outcrop in North Shanxi, China

Current methods of acquiring data from outcrops with tight gas rocks yield insufficient data. To resolve this, unmanned aerial vehicle (UAV) oblique photogrammetry was used to quantitatively assess the Pingtouxiang outcrop in the North Shanxi province, China. Rapid and flexible data acquisition, processing, and interpretation helped, develop a three-dimensional (3-D) geological model for a typical outcrop; here, the model of the delta deposit outcrop has 3-D coordinates that correspond to oblique image data, allowing for both the accurate location of field points and for measurements of the rock body. This provides the foundation for studying the stratigraphic architecture of the outcrop. Assessments of experimental sand plot contours and deposition patterns were carried out, in addition to the physical distribution of different sand bodies including superimposed distributary channels, superimposed distributary channels with sheet sand and bars or with continuous or isolated sheet sand. Different sand body types have varied scales and architecture patterns. Virtual well data, lithological calibration, and a geological model and database were used to establish 3-D stratigraphic and lithological models of a typical outcrop via sequential indicator simulation (SIS). Multi-point geostatistical simulations (MPS) were then used to establish a 3-D geological model of the sand bodies that is more in line with our current understanding of the geology of the outcrop area.

Prediction of Sedimentary Microfacies Distribution by Coupling Stochastic Modeling Method in Oil and Gas Energy Resource Exploitation

In view of the problem that a single modeling method cannot predict the distribution of microfacies, a new idea of coupling modeling method to comprehensively predict the distribution of sedimentary microfacies was proposed, breaking the tradition that different sedimentary microfacies used the same modeling method in the past. Because different sedimentary microfacies have different distribution characteristics and geometric shapes, it is more accurate to select different simulation methods for prediction. In this paper, the coupling modeling method was to establish the distribution of sedimentary microfacies with simple geometry through the point indicating process simulation, and then predict the microfacies with complex spatial distribution through the sequential indicator simulation method. Taking the DC block of Bohai basin as an example, a high-precision reservoir sedimentary microfacies model was established by the above coupling modeling method, and the model verification results showed that the sedimentary microfacies model had a high consistency with the underground. The coupling microfacies modeling method had higher accuracy and reliability than the traditional modeling method, which provided a new idea for the prediction of sedimentary microfacies.

The role of different sources of uncertainty on the stochastic quantification of subsurface discharges in heterogeneous aquifers

The quantification of subsurface discharges may significantly be affected by multiple sources of uncertainty, especially in highly heterogeneous aquifers. In this work, subsurface discharges within alluvial aquifers located in the province of Lecco (Lombardy, Northern Italy) are quantified relying upon a Monte Carlo (MC) framework under geological and conditioning data uncertainty sources. These are respectively employed through different geological conceptual models, whose facies spatial distributions are simulated through Sequential Indicator Simulations methodology (SISIM), and by conditioning stochastic facies simulations with different data constraints. Stochastic porosity fields are then achieved upon facies simulated fields by associating average porosity values to each local simulated facies. Then, stochastic subsurface discharge fluxes are quantified under steady hydraulic conditions upon saturated hydraulic fields, inferred from porosity ones. Then, the study of subsurface discharges is pursued via the Generalized Extreme Values (GEV) approach. This method enables both to interpret peak-over-thresholds (POTs) discharges spatial distribution, and to study the role geological and data uncertainty sources on their quantification, for example studying discharges conveyed towards a lake through the surrounding aquifer. These uncertainty-based analyses show that subsurface discharges clearly convey in correspondence of main alluvial aquifers, as well as the tendency to be collected along preferential pathways. Furthermore, the inspection of POTs empirical probability spatial distributions highlights that the geological uncertainty plays as a primary uncertainty source rather than the conditioning data. The latter plays a minor role, e.g. between different employed models, only in presence of a relevant geological difference between analyzed ones. These results are consistent with other kinds of information, such as hydrogeological sections and available piezometric maps, providing novel contributions to understanding how to quantify subsurface discharges in poorly monitored aquifers or how to treat various sources of uncertainty.

A v-transformed copula-based simulation model for lithological classification in an Indian copper deposit

Copula functions are widely used for modeling multivariate dependence. Since the multivariate data may not necessarily be linear and Gaussian, the copula model is very often brought into the picture for modeling such multivariate phenomena. The lithological classification in spatial domain is a class of problems dealing with categorical variables. A generalized class of copula model is effective for such classification tasks. In this paper, a non-Gaussian copula (v-transformed copula) model has been used for lithotype classification of an Indian copper deposit. Coupling of Markov chain Monte Carlo (MCMC) simulation and copula discriminant function is performed for this purpose. Specifically, four lithotypes, e.g., granite, quartz, basic, and aplite are simulated in the case study deposit. The efficacy of v-transformed copula discriminant function-based simulation is compared with those of Gaussian copula, t copula, and sequential indicator simulations. Finally, the classification accuracy of all the approaches is examined with ground-truth lithological classes obtained from blast hole information. The results show that the v-transformed copula simulation has a relatively higher classification accuracy (76%) than those of Gaussian copula (70%), t copula (69%), and sequential indicator (70%) simulations.

Modelling spatial uncertainty of geochemical anomalies using fractal and sequential indicator simulation methods

Mapping of geochemical anomalies is crucial to exploration and environmental geochemistry. The complex geochemical landscape, multiple geological sources and various secondary surficial processes impose a certain degree of spatial uncertainty in mapping of geochemical anomalies. Quantifying this uncertainty is significant for improving the efficiency of environmental monitoring or mineral prospecting. In this paper, sequential indicator simulation (SISIM) was used to assess local and spatial uncertainties of geochemical anomalies, and the concentration–area (C–A) fractal model was employed to determine the geochemical threshold prior to SISIM analysis. To illustrate the uncertainty of Ag geochemical anomalies, Ag concentration data of 1880 soil samples collected from NE of the Dong Ujimqin Banner district of Inner Mongolia, North China, was used in this study. Based on a set of simulation realizations of Ag concentrations, it was concluded that areas with low local (i.e. single-location) uncertainty of Ag concentrations entail low risk for mineral exploration. However, the spatial uncertainty for multi-locations showed that the joint probability statistics were stricter than local uncertainty. Therefore, combining local probability and spatial joint probability for delineating geochemical anomalies of Ag is more acceptable and reliable. The hybrid approach using the C–A fractal model and SISIM provides a new way to delineate anomalous areas by considering the uncertainty of spatial distributions of geochemical elements. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis

Probabilistic approach for seismic microzonation integrating 3D geological and geotechnical uncertainty

A novel probabilistic methodology for regional seismic site characterization is proposed and applied to a region with highly heterogeneous surficial geology and varying soil sediment thickness and stiffness. The method combines various sources of geological and geotechnical uncertainties to develop a three-dimensional (3D) shear-wave velocity ( Vs) model and evaluate the associated uncertainties. A 3D geological model of the unconsolidated deposits was developed using geostatistical interpolation and simulation methods. Sequential indicator simulations produced a quantitative geologic model that explicitly quantified geological uncertainties based on the likelihood of specific soil types occurring. In situ measurements and multivariate statistical analysis allowed the development of empirical correlations between Vs, geotechnical parameters, depth, and soil types. The resulting 3D Vs values were estimated on the basis of Vs-depth correlations and the probability of occurrence of each soil type. In this approach, the propagated uncertainty was also quantified by considering the combined variance. Seismic microzonation mapping was then conducted by transforming the 3D Vs model into two-dimensional (2D) maps that represent the spatial distributions of the time-averaged shear-wave velocity of the top 30 m ( Vs,30) and the fundamental site period ( T0), along with their respective uncertainties using Monte Carlo simulations. The results indicate that microzonation maps and their uncertainties are influenced by the thickness, occurrence probability, and geotechnical properties of soils. The proposed method can be used to assess the probabilistic seismic risk at local and regional scales in areas with geologically and geotechnically complex soil properties.

Geostatistical modeling of heterogeneous geo-clusters in a copper deposit integrated with multinomial logistic regression: An exercise on resource estimation

Resource estimation is the main and primary step in the development of a mining project. Principally, it is necessary to first identify the geological domains through boreholes, model them at unsampled locations, and then evaluate the grade(s) of interest inside each built domain. The traditional determination of these categorical domains over the sampling points is suboptimal as it considers mostly-one or two variables from core logging. This leads to the neglect of the influence of other significant variables. To circumvent the problem of estimation domain identification, spatially dependent clustering machine learning algorithms can be of great help in detecting such domains. However, one problem that may appear when using these techniques is that the resulting geo-domains (geo-clusters) obtained by the clustering technique might be heterogeneous and show a non-stationary property. The reason is that the aim of these spatially dependent techniques is to produce compact and spatially contiguous clusters, which are well suited to establishing non-stationary geo-domains. This makes the procedure of modelling challenging as it necessitates the use of advanced geostatistical techniques to propagate the heterogeneous geo-clusters at unsampled locations.An algorithm is presented in this study that employs a non-stationary sequential indicator simulation paradigm to model such complex variability of heterogeneous geo-clusters. Since the spatial trends of underlying geo-clusters are required in this simulation method, in this study, we propose the use of multinomial logistic regression to infer these trends. The algorithm was tested using an actual case study from a porphyry copper deposit in Iran, where Cu, Mo, Au, Rock Quality Designation (RQD), mineralization zones, alteration types, and rock types were employed to identify and spatially model the heterogeneous geo-domains in the entire deposit. The results were compared with a conventional sequential indicator simulation where no trend was used. An examination of the resulting maps using several evaluation criteria including visual inspection of the realizations, probability maps, reproduction of proportion of each geo-cluster, connectivity measures, and trend analysis, showed that the findings of the proposed algorithm were superior in modelling heterogeneous geo-domains.

Reservoir hydrocarbon potential of the Miocene Agbada Formation using RMS amplitude map and hydraulic flow unit model, Hatch Field offshore, Niger Delta Basin, Nigeria

The Miocene Agbada Formation is the hydrocarbon-producing unit in the Niger Delta region because it has excellent stratigraphic and structural traps. Understanding the reservoirs' hydraulic flow characteristics in the Agbada Formation is necessary. The flow of fluids in a reservoir is controlled by the effective porosity of the reservoir that can be spatially correlated with root mean square (RMS) amplitude and HFUs.The study aims to produce an RMS map and HFUs model for reservoir units D and E in the Hatch Field Offshore Niger Delta Basin and determine the reservoirs' hydrocarbon potential using the reservoir seismic data, core analysis, and well-log data. The seismic attribute of RMS amplitude was applied for both reservoir units D and E in the Hatch field. The calculated FZI was categorised using a conditional statement with two HFUs. The hydraulic flow unit model for both reservoir units was built utilising a simulation grid design of 157x100x5 for reservoir unit D and 204x120x5 for reservoir unit E. The sequential indicator simulation (SISIM) algorithm was applied to simulate the HFUs model for reservoir units D and E. The RMS amplitude maps show bright spots that are possible hydrocarbon prospects in P1, P2, and P3 for reservoir unit D. However, reservoir unit E has bright spots that are also likely hydrocarbon prospects here named P4 and P5. The brighter amplitudes observed in prospects P4 and P5 signify significant amounts of hydrocarbons in reservoir unit E. The results of the hydraulic flow units’ model is represented by the spatial variability of flow units within the reservoir. The coefficient of determination between well-log data and the model results range from 0.9258 to 0.9655 and 0.8586 to 0.9868 for reservoir units D and E, respectively. A litho-saturation cross plot comparing lithofacies and HFUs confirms that HFU 1 comprises sandstone while HFU 2 is composed of marine shale. The reservoirs' spatial distribution of hydraulic flow units shows that unit D has poor hydraulic flow characteristics and hydrocarbon potential due to the high proportion of marine shale facies in HFU 2 compared to HFU 1. Reservoir unit E predominantly has a higher proportion of sandstone in HFU 1 compared to HFU 2, making it a better reservoir with good hydrocarbon potential than reservoir unit D.

Geostatistical Ore Body Modeling on Uranium Mineralization in Remaja Sector, Kalan Area, West Kalimantan

Manual ore body modeling on Remaja Sector, Kalan, West Kalimantan generally takes a long time and is subjective. On the other hand, automatic modeling (implicit modeling) is faster, objective, and equipped with uncertainty factors. This study aimed to analyze the comparison between the geostatistical Sequential Indicator Simulation (SIS) ore body model to the manual ore body model. The lithology database was used as input for variogram analysis and SIS simulation. The directional variogram was used to construct an experimental variogram for the lithology with orientation data. The orientation of the lithologies corresponds to the anisotropy of their variogram map. The SIS was carried out in Block A and Block B with block sizes of 6×6×6 m3 and 5×5×5 m3 respectively. The simulation results were processed to produce a lithology probability model. By using maximum probability as block lithology, simulation results were well validated by the composite database histogram, the lithologies along the tunnel on the geological map of level 450 masl of Eko Remaja Tunnel., and the lithologies along boreholes. The weakness of the geostatistical ore body model was the results depending on the input parameters. Meanwhile, several advantages of the geostatistical ore body model were a faster processing process, equipped with an uncertainty factor, and the block size of the model has taken into account the distance between grade data so that it can be used directly for grade estimation. Quantitatively, the geostatistical ore body model had a higher average percentage of conformity to the lithology of the mineralized zone along the borehole than the manual ore body model.

Automated Hierarchical 3D Modeling of Quaternary Aquifers: The ArchPy Approach

When modeling groundwater systems in Quaternary formations, one of the first steps is to construct a geological and petrophysical model. This is often cumbersome because it requires multiple manual steps which include geophysical interpretation, construction of a structural model, and identification of geostatistical model parameters, facies, and property simulations. Those steps are often carried out using different software, which makes the automation intractable or very difficult. A non-automated approach is time-consuming and makes the model updating difficult when new data are available or when some geological interpretations are modified. Furthermore, conducting a cross-validation procedure to assess the overall quality of the models and quantifying the joint structural and parametric uncertainty are tedious. To address these issues, we propose a new approach and a Python module, ArchPy, to automatically generate realistic geological and parameter models. One of its main features is that the modeling operates in a hierarchical manner. The input data consist of a set of borehole data and a stratigraphic pile. The stratigraphic pile describes how the model should be constructed formally and in a compact manner. It contains the list of the different stratigraphic units and their order in the pile, their conformability (eroded or onlap), the surface interpolation method (e.g., kriging, sequential Gaussian simulation (SGS), and multiple-point statistics (MPS)), the filling method for the lithologies (e.g., MPS and sequential indicator simulation (SIS)), and the petrophysical properties (e.g., MPS and SGS). Then, the procedure is automatic. In a first step, the stratigraphic unit boundaries are simulated. Second, they are filled with lithologies, and finally, the petrophysical properties are simulated inside the lithologies. All these steps are straightforward and automated once the stratigraphic pile and its related parameters have been defined. Hence, this approach is extremely flexible. The automation provides a framework to generate end-to-end stochastic models and then the proposed method allows for uncertainty quantification at any level and may be used for full inversion. In this work, ArchPy is illustrated using data from an alpine Quaternary aquifer in the upper Aare plain (southeast of Bern, Switzerland).

Uncertainty health risk assessment and regional control of drinking water: a case study of Hanyuan County, southwest mountainous area, China.

To evaluate the health risks of drinking water in Hanyuan County, 96 samples of peripheral drinking water were collected from 30 sites in the area. The samples were then analyzed for chemical properties including Fe, Mn, NH3-N, [Formula: see text], F-, Pb, Hg, As, Cr(VI), Cd, and so on. The health risks of ten trace elements in drinking water were probabilistically assessed using the health risk assessment model and Monte Carlo simulation. On this basis, sequential indicator simulations were used to classify the health risk levels of drinking water in the region, to conduct hierarchical management and control. The results showed that except for [Formula: see text], all other indicators met World Health Organization standards and China's drinking water sanitation standards. Drinking water presents a specific carcinogenic risk to adults, and the cumulative contribution of As and Cr(VI) exceeds 95%, and has a specific non-carcinogenic risk to children if the cumulative contribution of F-, [Formula: see text], and As exceeds 90%. Grade I, II, and III non-carcinogenic risk areas accounted for 0.89%, 24.72%, and 74.39% of the total area of Hanyuan County, respectively, while grade I, II, and III carcinogenic risk areas accounted for 27.71%, 45.56%, and 26.73% of the total Hanyuan County area, respectively. Finally, according to the health risk characteristics of each control area, corresponding zoning control suggestions were proposed.