Sequential Indicator Simulation Research Articles

A case study of 3D geomodelling of Frontier Formation Second Wall Creek Sand, Teapot Dome, Wyoming, USA

The research focuses on 3D seismic geomodelling of Cretaceous shoreface reservoir sands of the Frontier formation, Teapot Dome, Wyoming, USA. 3D geomodelling helps in understanding the facies distribution (lateral and vertical) of Frontier Formation Second Wall Creek (F2WC). We have adopted an integration of petrophysical analysis, seismic interpretation and geostatistical evaluations to map the facies. In geostatistical modelling the data distribution of the discrete reservoir properties has been performed using the sequential indicator simulation (SIS) while the continuous data modeled with the help of Sequential Gaussian Simulation (SGS). Total 32 wells have been used in this study along with a synthetic seismogram generation for the time-domain correlation of well-tops with the seismic reflections using wells (67–1-TpX-10 and 25–1-X-14). The study area is a structurally deformed asymmetrical doubly plunging dome of the lower Cretaceous to Eocene, Laramide deformation with EW trending faults that can be easily identified by modelling techniques mentioned. In this study, two blind wells are also used to verify the model, the horizons are also verified using the TDR relation provided for 67–1-TpX-10. Velocity modelling also helped in the verification of correct depth of each formation with the well tops. F2WC is attributed with average permeability ranging between 0 and 100 mD, average porosity is 18%, the average water saturation is 30%. The results suggest that the southern part of Teapot Dome is more prolific due to better spatial distribution of porosity, permeability and less water saturation within the reservoir model.

Reservoir facies and porosity modeling using seismic data and well logs by geostatistical simulation in an oil field

Reservoir characterization of petroleum reservoirs can be claimed as one of the most important parts of reservoir management for optimized production and future developments. Through reservoir evaluation, geological zoning for a better comprehension of subsurface structure is needed. For developing a model, porosity plays a vital role; there are two common methods for obtaining this parameter, core samples and well logging. However, the results of these methods are in well scale which cannot be used through field scale modeling. A solution can be the combining seismic field data well log data, which makes it possible to estimate the reservoir properties in field scale. In this study, multi-attribute analyses were applied based on multilayer perceptron to determine the reservoir facies alteration and heterogeneity in the Ghar reservoir of the Hendijan oil field located in the Persian Gulf. Facies modeling was done through the sequential indicator simulation (SIS) algorithm which coupled with the possible trend and indicator kriging (IK) as geostatistical methods. Within the comparison of these two generated models with core facies, the obtained accuracy of SIS algorithm coupled with the possible trend and indicator kriging are 94% and 72%, respectively. Porosity distribution was also done by the sequential Gaussian simulation (SGS) algorithm which resulted the average porosity of 18% in Ghar formation. The SIS method results are compatible with the porosity distribution model obtained from the SGS simulation. The final results prove the robustness of the applied methods for facies and porosity modeling.

Application of sequential indicator simulation, sequential Gaussian simulation and flow zone indicator in reservoir-E modelling; Hatch Field Niger Delta Basin, Nigeria

The study was carried out with the aim of predicting uncertainty using Sequential Indicator Simulation (SISIM) for lithofacies modelling, sequential Gaussian simulation (SGSIM) for reservoir property modelling and inferring the depositional environment using Flow Zone Indicator (FZI). Five well logs, core analysis data and 3D seismic data were used for the study. Reservoir sand named “Reservoir-E” was correlated across the five wells in the field, and its surface was mapped on the seismic sections from the 3D cube. Petrophysical evaluations revealed average, net to gross, porosity, permeability, the volume of shale, water saturation, hydrocarbon saturation (SH), Reservoir Quality Index (RQI) and FZI as 0.70, 0.29, 2342 mD, 0.158, 0.50, 0.50, 2.44 and 5.62 μm, respectively. Structural interpretations on seismic sections revealed five major faults which trend NE-SW, NW-SE and W-E. A 3D grid with dimension 204 × 120 × 5 = 122,400 was constructed to accommodate our model. Validation of lithofacies models shows a correlation coefficient of 89.43–92.98% for well data and model. The property model shows variability in effective porosity and permeability 0.24 to 0.38 and 560 to 5600 mD. The low amount of SH in well HT-2 and HT-3ST1 is attributed to the presence of baffles with low reservoir quality index with 1.81 and 1.67 for RQI and 5.05 and 5.27 for FZI as compared to well HT-1, HT-4ST1 and HT-5 with RQI of 3.61, 2.71 and 2.41 and FZI of 6.25, 5.85 and 5.66. The environment of deposition is interpreted to be mouth bar-upper shoreface. High porosity and permeability values are linked with high FZI and RQI which are targets for exploitation.

An Uncertainty Assessment of Human Health Risk for Toxic Trace Elements Using a Sequential Indicator Simulation in Farmland Soils

Toxic trace elements in farmland soils are potential threats to human health. In this study, we collected soil samples from the farmlands of southern Guangzhou. We used a sequential indicator simulation (SIS) to deal with the problem of skewed distribution in the sample data. We assessed the human health risks, as well as the uncertainties, of five toxic trace elements: arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and mercury (Hg). The results were as follows: (1) The risk indices of two trace elements (Cd and Hg) were less than the standard threshold, which means that there was no human health risk due to Cd and Hg in the study area. However, the maximum risk indices of As, Cr, and Pb exceeded the standard threshold. In particular, the maximum risk index of Pb was twice the standard threshold; (2) The risk probabilities of As and Cr were less than 25% in most areas, and only a few parcels of farmland have a 100% risk probability. The risk map of Pb was used to identify contiguous areas of high-risk probability (i.e., 75%–100%) in the center of the study area. (3) E-type estimation by the SIS method overestimates the risk when the number of samples with concentrations above the threshold have a large proportion of total samples. Our conclusions are as follows: (1) The simulation results show that areas with high-risk indices were concentrated in the Panyu District, which is close to the Pearl River and the core urban area of Guangzhou; (2) Except for Pb, these trace elements are not likely to pose health risks in southern Guangzhou; (3) This study considers the risk probability found with the SIS method to be more reliable for visualizing regional risk.

Application of Multi-Resolution Graph-based Clustering for electrofacies prediction: a case study from the Horn River Shale, British Columbia, Canada

The Horn River Basin is a major shale gas play in British Columbia, Canada. An important component for identifying productive zones is to determine lithofacies. Quantitative core-based lithofacies classifications distinguish reservoirs and non-reservoir quality zones. In this study, Elemental capture spectroscopy (ECS) log data, XRD, and TOC data were integrated with core-description data to define the shale gas development interval in the Horn River Shale. Six lithofacies were determined and used as a training data for the supervised electrofacies classification. The Multi-Resolution Graph-based Clustering method (MRGC) was useful for prediction of electrofacies. The MRGC facilitated estimation of the electrofacies from the well logs of the non-coring wells based on our current construction model. We constructed a supervised MRGC model from well A. The probability scores of the electrofacies were 83%. This method was applied to non-coring wells B and C. Lateral variations in the facies were inferred using an electrofacies correlation or seismic-scale spatial model. Stochastic methods were applied to build a 3D facies model due to the insufficient electrofacies data. To increase the accuracy and applicability we used seismically derived-trend data (density and P-wave sonic inversion, envelope attributes) for sequential indicator simulation (SIS) modeling. After constructing a facies model, the faintly laminated siliceous mudstone (FLSM) and homogeneous siliceous mudstone (HSM) facies were shown to be dominantly distributed throughout the Evie Member and Muskwa Formation. These facies contain high silica compared to clay and higher TOC contents which are generally the main targets of a shale gas play. Therefore, the FLSM and HSM facies of the Muskwa Formation and Evie Member are potentially productive facies for shale gas development in the Horn River Shale.

The influence of connected heterogeneity on groundwater flow and salinity distributions in coastal volcanic aquifers

Geological heterogeneity is a key factor affecting rates and patterns of groundwater flow and the evolution of salinity distributions in coastal aquifers. The hydrogeologic systems of volcanic aquifers are characterized by lava flows that can form connected geologic structures in the subsurface. Surface-based geostatistical techniques were adopted to generate geologically-realistic, statistically equivalent model realizations of a hydrogeologic system based on that of the Big Island of Hawaii (conduit models). The density-dependent groundwater flow and solute transport code SEAWAT was used to perform 3D simulations to investigate subsurface flow and salt transport through these random realizations. Statistically equivalent geological systems generated with sequential indicator simulation (SIS) and equivalent homogeneous systems were also simulated for comparison. Simulation results show that conduit realizations tend to have more complex salinity distributions with larger mixing zones for which the center of mass is farther offshore compared to homogeneous and SIS realizations. The zone of groundwater discharge is also farther offshore than for homogeneous and SIS models, with higher point fluxes of both fresh and saline water and greater spatial variability. This highlights the importance of the geometry of geologic features in coastal groundwater flow and solute transport processes in highly heterogeneous aquifers. It also points to the importance of preferential flow, with implications for both coastal groundwater resource management and solute fluxes to the ocean.

Hydrocarbon Reservoir Characterization of ‘UDI’ Field, Western Niger Delta

The increasing demand for petroleum products has posed a challenge to the search for oil and gas. This search for hydrocarbon has developed due to advances in computational techniques to evaluate the probability of hydrocarbon proneness of a basin, thereby limiting the risk factor associated with hydrocarbon. This study was therefore designed to assess the hydrocarbon potential and generate a static reservoir model of UDI Field, Onshore Niger Delta.
 Well, the correlation was carried out to establish stratigraphic continuity of the reservoir sand bodies. The identified potential reservoir intervals were tied to the seismic data using available check shot survey data. With a good match achieved, seismic events were interpreted through paying attention to reflection continuity, amplitude and frequency. Interpreted horizons were converted to surfaces using a convergent interpolation algorithm. Faults within the Field showed a dominant East-West trend with two (2) major faults and five (5) minor ones. A Pixel-based facies model was built based on the normal distribution of the upscaled lithofacies log using the Sequential Indicator Simulation algorithm. Petrophysical models were built by constraining the petrophysical logs to the facies models using Sequential Gaussian simulation algorithm. 
 Four potential reservoir intervals, A100, A125, A150 and A200 were delineated. Average petrophysical parameters were computed for all the four intervals and the results revealed the reservoir intervals to be of good quality. Sand A100 has the highest average porosity value of 29.4%, while Sand A200 has the lowest value of 25.3%. Net-to-gross ratio also follows the pattern of decreasing value with depth. Sand A150 has the highest average gross thickness value, 170.4 m, while Sand A200 has the least thickness of 80.5 m. The net-to-gross ratio preserved the pattern of gross thickness and this resulted in Sand A150 still having the highest Net thickness and Sand A200 having the least Net sand thickness. The relatively large net sand thicknesses, high net-to-gross ratio values and the high porosity values all support the reservoir intervals within UDI Field to be of good quality.
 Extrapolations of reservoir properties away from good control honored the geological interpretation of reservoir Sand A125 thereby reducing the subsurface reservoir uncertainties.
 The availability of pressure data of the reservoir will help in establishing whether the reservoir is compartmentalized and hence the model can be updated to accommodate the effect of compartmentalization.

A Three-Dimensional Geotechnical Spatial Modeling Method for Borehole Dataset Using Optimization of Geostatistical Approaches

A large amount of geotechnical investigation data is essential for the highly reliable design of geotechnical structure at a construction site. The number of geotechnical investigations, however, has been generally insufficient and spatially biased owing to economic and spatial-temporal limitations. In this study, a geotechnical three-dimensional spatial modeling was implemented using an optimized geostatistical interpolation approach at a bridge construction site in the south-central part of the Korean peninsula. The geotechnical investigation data were collected and standardized for the construction of a geo-database. For the site-specific stratification, a kriging-based integration of the geo-layers and the seismic velocity from a seismic refraction survey were applied. The value from a standard penetration test (SPT)-N of an uninvestigated location was predicted using parametric and nonparametric geostatistical methods. We accomplished three-dimensional spatial interpolations using ordinary kriging, a sequential Gaussian simulation with a normal score transformed dataset, and a sequential indicator simulation using the geodatabase. A leave-one-out cross validation was carried out to quantitatively evaluate the reliability of the three-dimensional modeling. Finally, a three-dimensional geotechnical spatial model assigned with subsurface stratification and SPT-N values was constructed using the sequential Gaussian simulation.

Quantitative characterization of diagenetic reservoir facies of the Karamay alluvial fan in the Junggar Basin, western China

The distribution of the remaining oil in developed oilfields is primarily influenced by the existing diagenetic reservoir facies (DRFs), which encompass the effects of both the sedimentary environment and the local diagenetic processes on the reservoir quality. We divided the Lower Karamay Formation reservoir samples into nine DRFs according to characteristics such as porosity, permeability, diagenetic intensity, and rock type. After applying the sequential indicator simulation methodology, we were able to create three-dimensional (3D) geological models of the DRFs. Based on these models, we can see that, during phases A and B of eodiagenesis, the prevailing DRF is a sandstone/sandy conglomerate that is moderately to weakly dissolved, moderately cemented, and characterized by moderate porosity and permeability values. The highest quality DRF, which emerged during the early stage of mesodiagenesis, is a sandstone/sandy conglomerate that is weakly compacted/cemented, moderately-to-strongly dissolved, and characterized by very high porosity and permeability values. By providing high-quality analyses of the DRFs in Block 6 of the Karamay oilfield, we provided vital information that allowed the operation to more efficiently target and extract oil from reservoir units that are already in production.

Petrofacies prediction and 3-D geological model in tight gas sandstone reservoirs by integration of well logs and geostatistical modeling

Abstract These effective reservoir rocks that can have sufficient porosity and permeability and hydrocarbon accumulations determine economic reserves and high yield of tight oil and gas. Understanding the origin and distribution of these effective reservoir rocks is important for sorting out “sweet spots” in tight reservoirs. Multiple-scale analyses from the microscopic, via drill core to single wellbore have been conducted to examine reservoir heterogeneity of the Upper Paleozoic Shanxi Formation in the southeastern Ordos Basin. Five sandstone petrofacies have been defined in terms of texture and framework composition, detrital matrix, diagenesis and pore types: quartz arenite, tuffaceous quartz arenite, ductile lithic-lean sub-litharenite, ductile lithic-rich sandstone and tightly carbonate-cemented sandstone. The various petrofacies show fundamentally different pathways of diagenetic and reservoir-quality evolutions, due to the complexity of texture and detrital composition in the Shanxi Formation, directly related to the provenance and depositional environment. Quartz arenites and ductile grain-lean sub-litharenites both form effective reservoir rocks. By translating petrofacies to well-log signatures corrected from petrographic and core analysis data, a model based on factor analysis has been built to predict petrofacies at the well scale. For investigating the distribution of each petrofacies at the field scale, sequential indicator simulation method has been used to construct a 3-D petrofacies model with facies conditioning. The isopach maps of quartz arenites and ductile lithic-lean sub-litharenites have been made from the 3-D petrofacies model to quantify their areal distributions, and to define the “sweet spots” by combining isopach maps of these effective reservoir rocks and gas production data from well tests. The concept of reservoir petrofacies is an important tool for the characterization of tight sandstone reservoirs.

Integrated 3D modeling of Quaternary sediments in the Beijing Plain, based on a sequential indicator simulation

The study of Quaternary sediments has long been a focus for geologists, primarily because they are closely aligned with urban safety assessment, energy exploitation and sustainable development. The Beijing Plain was selected for this study. Using existing drilling data and knowledge of the sedimentary characteristics in this study area, a geological model was developed with Petrel software using the sequential indicator simulation algorithm. The main aims were firstly, to integrate ArcGIS and Petrel with drilling information, a digital elevation model, a stratum sedimentary thickness plan and other multi-platform data on the same platform. Secondly, establish a lithology variogram model and then construct a quaternary lithology model based on the variogram model and finally, use the established lithology model to preliminarily analyze the lateral and vertical distribution rules of Quaternary lithology in the study area. These results provide new methods for the establishment of geologic modeling during the preliminary stages when studying engineering geology. The results will also provide baseline information for later research.

Depositional conditioning of three dimensional training images: Improving the reproduction and representation of architectural elements in sand-dominated fluvial reservoir models

Fluvial deposits create significant hydrocarbon reservoirs, although their characterisation can be difficult due to their differing scales of heterogeneity. Whilst numerical modelling methods have advanced to statistically honour fluvial input datasets, geologically realistic features are often lost, impacting hydrocarbon recovery predictions. Two dimensional training images are often used to dictate what heterogeneity is inputted into multi-point statistics based reservoir models. In this study, a three dimensional training image is built, based upon depositional conditions derived from outcrop and modern satellite imagery data of a fluvial system. The aims of this study are to: identify the heterogeneity within the modern and outcrop data and to replicate it in a three dimensional training image; to model such heterogeneity using object-based, sequential indicator simulation and multi-point statistics; and to qualitatively and quantitatively (through static net-connectivity testing) analyse the reproducibility and geological realism of the generated reservoir models. Digital photogrammetric data from Tuscher Canyon, Utah, of the Lower Castlegate Sandstone and satellite imagery from the Jamuna River, northern India, are used to depositionally condition a three dimensional training image. This training image was then used to generate the multi-point statistics models, which were then tested against more traditional object-based and sequential indicator simulation reservoir models. Results indicated that object-based models realistically reproduced heterogeneous architectural elements, however, the connectivity of net-reservoir elements were unrealistically shaped and over-connected. The sequential indicator simulated models produced unrealistic heterogeneous architectural elements and overestimated the connectivity of net-reservoir elements. The multi-point statistical models realistically reproduced heterogeneous architectural elements geometries and the connectivity of net-reservoir elements. Study implications suggest that, based upon limited data, depositional conditioning can generate three dimensional training images to produce reservoir models that are both geologically realistic and reproducible. • Heterogeneity within the Lower Castlegate of Tuscher Canyon, Utah is characterised. • MPS, SIS and OBM models of fluvial reservoirs are compared. • MPS is found to produce the most realistic and reproducible results.

Uncertainty quantification of structural and geotechnical parameter by geostatistical simulations applied to a stability analysis case study with limited exploration data

There are many uncertain factors impacting stability analyses of rock structures. Quantification of these factors significantly impact the engineering decision making process. Some of these factors are spatially distributed within the geological domain, and the quantification of the spatial uncertainty is beneficial for stability analysis of the structure. This paper proposes an integrated methodology for a stability analysis of rock structure by incorporating spatially varying factors. This research models two key factors: one categorical structural parameter (fault zones) and one continuous geotechnical parameter (rock quality designation (RQD)). Two geostatistical simulation algorithms i.e., sequential indicator simulation and sequential Gaussian simulation are applied to quantify the spatial uncertainty by generating multiple equiprobable simulation maps of fault zones and RQD values. A slope stability analysis is performed by modeling the spatially varying fault zones and RQD. The proposed method is validated using a synthetic data set. The results from the synthetic data show the stability value of the synthetic data closely match with stability value calculated from simulated data. Finally, the proposed method is applied to a slope stability analysis for an open pit mine design where limited exploration data is available. Geostatistically simulated RQD maps from the case study mine are analyzed to determine a safe slope angle of 44° for the open pit mining operation. The proposed method can be applied for quantifying other geotechnical risk and integrated within any stability analysis framework.

Depositional regimes and reservoir architecture characterization of alluvial fans of Karamay oilfield in Junggar basin, Western China

We analyzed the lithology and sedimentary structures of the lower Karamay formation alluvial fan of Karamay oilfield in Junggar basin and subdivided its lithofacies into six types. The depositional regimes of the lower Karamay formation regressive alluvial fans contain (1) cohesive debris flows, (2) sandy debris flows, (3) grain flows, (4) turbidity currents, and (5) traction currents in the light of sedimentary fabrics and lithofacies association. In terms of lithofacies association, depositional succession, boundary surface hierarchy and wireline logs, fourth-order reservoir architectures for this alluvial fan are composed of nine types: (1) trench channel conglomerates, (2) trench sandy conglomerates, (3) trench proximal sandbody, (4) trench sheetflood lithosome, (5) trench distal sandbody, (6) braided channel, (7) channel gravel bar, (8) overflow sandbody, and (9) runoff channel. On the basis of the reservoir architecture identification of all wells, we established a three-dimensional (3D) architectural element model of the lower Karamay formation with a method of sequential indicator simulation to reveal the reservoir architecture spatial distribution. Integrated three-dimensional reservoir architecture models with contemporary depositional characteristics of alluvial fans, we concluded the depositional patterns of reservoir architectures for different alluvial fan subfacies, which shows the geometric features and aspects of the reservoir architectures. This information provided a technical foundation for the remaining oil prediction, development strategy optimization, and oil recovery enhancement of the conglomeratic oil reservoirs at the high water-cut stage.

Application of Sequential Indicator Simulation in Geological Study of X Oilfield in Zhujiangkou Basin

Sequential indicator simulation is a commonly used method for discrete variable simulation in 3D geological modeling and a widely used stochastic simulation method, which can be used not only for continuous variable simulation but also for discrete variable simulation. In this paper, the X Oilfield in the western South China Sea is taken as an example to compare the sequential indicator simulation method and the Indicator Kriging interpolation method. The results of the final comparison show that the results of the lithofacies model established by the Indicator Kriging deterministic interpolation method are overly smooth, and its coincidence rate with the geological statistical results is not high, thus cannot well reflect the heterogeneity of the underground reservoir, while the simulation results of the lithofacies model established by the sequential indicator stochastic simulation method can fit well with the statistical law of the well, which has eliminated the smoothing effect of Kriging interpolation, thus can better reflect the heterogeneity of the underground reservoir. Therefore, the sequential indicator simulation is more suitable for the characterization of sand bodies and the study of reservoir heterogeneity.

Identifying quantitative sources and spatial distributions of potentially toxic elements in soils by using three receptor models and sequential indicator simulation.

Determining the reliable source contribution and spatial distribution of potentially toxic elements (PTEs) is a focal issue for soil regulation and remediation. For this purpose, three receptor models, US-EPA positive matrix factorization (EPAPMF), weighted alternating least squares positive matrix factorization (WALSPMF), and non-negative constrained absolutely principle analysis (NCAPCA), were used to a dataset consisting of ten PTEs (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, and Zn) for source apportionment. Hazardous areas of ten PTEs were delineated using sequential indicator simulation (SIS) and uncertainty analysis. Three factors for ten PTEs were derived by three receptor models with a one-to-one correspondence between the factors. To obtain more appropriate results, the three receptor models were combined to calculate the ensemble-average source contributions. As, Co, Cr, Cu, Mn, and Ni were derived from a natural source with ensemble-average contributions higher than 85.72%. Cd, Hg, Pb, and Zn were contributed by both parent material and anthropogenic influence. More than half of Hg concentrations were associated with atmospheric deposition caused by human emissions. The concentrations of 28.04% for Cd, 20.74% for Hg, 43.49% for Pb, and 23.71% for Zn were associated with human inputs including agriculture practice, industrial activities, and vehicle emissions. The maps of spatial distribution generated by the SIS indicated that parent materials controlled the spatial distributions of As, Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. 27.1% and 32.1% of the total area for Cd and Hg were identified as hazardous areas exceeding 1.5 times background values of Shandong province.

Application of geostatistics in facies modeling of Reservoir-E, \u201cHatch Field\u201d offshore Niger Delta Basin, Nigeria

Lithofacies are very influential in the transmission of fluids within the reservoir. The objective of this study is to use geostatistical techniques of sequential indicator simulation (SISIM) a variogram-based algorithm (VBA), single normal equation simulation (SNESIM) and filter-based simulation (FILTERSIM) of multiple-point geostatistics (MPG) in developing realistic facies model. A reservoir sand package “Reservoir-E” was correlated across five wells in the field. Synthetic seismogram of well HT-1 was generated, and Horizon E picked on seismic section to produce time and depth surfaces of the reservoir. The conditional if statement to generate lithofacies was applied on the extracted volume of shale data within “Reservoir-E,” and the data were inputted in Stanford Geostatistics Modeling Software for facies modeling. The first realization from SISIM was converted to a training image used for MPG. Visually, the MPG algorithm of SNESIM and FILTERSIM produced realization that is substantially better and more realistic than the VBA of SISIM. The magnitude of correlation coefficients of algorithms was carried out using the mean and variance of realizations, the results revealed mean and variance magnitude of correlation coefficients between SISIM and SNESIM with 0.8933 and 0.9637, SISIM and FILTERSIM with 0.8639 and 0.5097 and SNESIM and FILTERSIM with 0.9717 and 0.8603. The results revealed a very good mean and variance magnitude of correlation coefficients between SISIM and SNESIM; good between SISIM and FILTERSIM; and very good mean and variance correlation coefficient between SNESIM and FILTERSIM. The qualitative interpretation of the model built with SNESIM and FILTERSIM clearly detects lithofacies in the field which makes them a better algorithm in facies modeling.

Sequential indicator simulation for a three-dimensional distribution of hydrofacies in a volcano-sedimentary aquifer in Mexico City

The Mexico City aquifer is a complex mix of alluvial deposits and volcanic rocks overlapped by an aquitard composed of lacustrine deposits. To characterize this heterogeneous hydrogeologic system, a three-dimensional model of the distribution of hydrofacies is constructed using borehole lithological records. The analysis is based on 111 borehole logs with an average depth of 300 m, in an area of 234 km2, providing a nominal scale of resolution of 2.1 km in the plane and 2-m resolution in the vertical direction. These records were discretized to generate a georeferenced dataset of 13,518 points associated with a lithological category; nine lithological categories were observed. These categories were subsequently grouped into four hydrofacies: A and B, grouping low-permeability lithological categories (lacustrine and volcano-sedimentary materials, respectively); and C and D, grouping high-permeability lithological categories (volcanic rocks and alluvial deposits, respectively). The database was analyzed in terms of proportion of hydrofacies at depth, distribution of layer thickness, and behavior of experimental horizontal and vertical variograms. The experimental variograms of each hydrofacies were fitted to exponential models via minimization of cross-validation errors. Three-dimensional models of probability of occurrence of each hydrofacies and the combined distribution of hydrofacies were then constructed via ensemble averaging of 1,000 realizations obtained by sequential indicator simulation. The potential use of this model for water management, modeling land subsidence, and groundwater pollution is discussed.

Identification of electrical and petrophysical rock types based on core and well logs: Utilizing the results to delineate prolific zones in deep water sandy packages from the Shah Deniz gas field in the south Caspian Sea basin

The construction of a geological model using geostatistical simulation methods plays a key role in reservoir characterization. One of the most important parts of the reservoir studies is the discovery of high reservoir quality areas which in turn are dependent on the depositional properties of reservoir formations. The main properties of rocks are usually defined by descriptive studies in the field or on cored intervals (lithofacies) and in more detail by laboratory studies (petrofacies and microfacies). Considering that the core data are not available in all drilled wells, the main focus is on well log data which exist in all wells, continuously. In the current study, with focus on deep sandy reservoir deposits in the southern area of the Caspian Sea, reservoir rocks were characterized based on the integration of the results from core description, thin section studies and petrophysical analysis of well logs in one of the sandstone fields with five members. Balakhany and Fasila sand members are the deepest sandstone members of the field. First petrofacies were identified based on core description and thin section studies. In the next step, the electrical facies and electro-petrophysical facies were investigated using a combination of log data and petrophysical parameters based on Multi-Resolution Graph Base Clustering (MRGC) method. The results from this stage in combination with identified petrofacies were used for the construction of a reservoir static model. Finally, a 3D model of reservoir facies was obtained in each reservoir zone by using variogram analysis and sequential indicator simulation (SIS). The constructed facies model suggests that the high reservoir quality facies are well distributed in the sandy package 3 (SP3) and Fasila B.

High-resolution geostatistical modeling of an intensively drilled heavy oil reservoir, the BQ 10 block, Biyang Sag, Nanxiang Basin, China

Abstract Establishing a high-resolution subsurface model is the ultimate goal of reservoir characterization. The “BQ 10 Block”, in Biyang Sag of Nanxiang Basin in China, is a data-rich mature field with heavy oil deposits penetrated by over two hundred wells with an average well spacing of 75m. The aim of this study is to establish a three-dimensional high-resolution model of the BQ 10 reservoir and to accurately and efficiently derive its geological and geometrical properties. A geostatistical modeling study was carried out in combination with stratigraphic correlation, core observations, log interpretation, lithology estimation, and sedimentary facies analysis. Based on core and wireline log data, lithology and conventional well logs (i.e. resistivity, gamma ray, and spontaneous potential) were correlated first. Lithofacies data at cored well locations were grouped into petrofacies units based on reservoir quality data. Using a neural network method, petrofacies at uncored wells was predicted based on correlation with conventional well logs. For different petrofacies, statistical analyses revealed correlation between laboratory porosity and permeability with acoustic well logs. A petrofacies model was built alternatively with indicator kriging, truncated Gaussian simulation (TGS), and sequential indicator simulation (SIS). SIS with a 3D trend model was identified to give the most reasonable petrofacies distribution. Using this model, facies-controlled petrophysical property modeling was performed using sequential Gaussian simulation (SGS). A validation procedure based on “minimum acceptance criteria” was employed to ensure that the SIS/SGS realizations are consistent. For this data-rich reservoir, this study demonstrates an effective application of the standard geostatistical modeling techniques on constructing high-resolution reservoir models of a heavy oil reservoir. The final suite of models reveal a likely range of reservoir facies and petrophysical property distributions for BQ 10, which can be useful for designing heavy oil recovery operations.