Sequential Gaussian Simulation Method Research Articles

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.

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

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

Risk pre-assessment method for regional drilling engineering based on deep learning and multi-source data

Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties. Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a hot research topic and frontier in this field. However, due to the small number and uneven distribution of drilled wells in new exploration areas and the lack of sample data related to risk, the training model has insufficient generalization ability, and thus the prediction is not effective. In this paper, a drilling risk profile (depth domain) rich in geological and engineering information is constructed by introducing a quantitative evaluation method for drilling risk of drilled wells, which can provide sufficient risk sample data for model training and thus solve the small sample problem. For the problem of uneven distribution of drilling wells in new exploration areas, the concept of virtual wells and their deployment methods were proposed. Besides, two methods for calculating rock mechanical parameters of virtual wells were proposed, and the accuracy and applicability of the two methods are analyzed. The LSTM deep learning model was optimized to tap the quantitative relationship between drilling risk profiles and multi-source data (e.g., seismic, logging, and rock mechanical parameters). The model was validated to have an average relative error of 9.19%. The quantitative prediction of the drilling risk profile of the virtual well was achieved using the trained LSTM model and the calculation of the relevant parameters of the virtual well. Finally, based on the sequential Gaussian simulation method and the risk distribution of drilled and virtual wells, a regional 3D drilling risk model was constructed. The analysis of real cases shows that the addition of virtual wells can significantly improve the identification of regional drilling risks and the prediction accuracy of pre-drill drilling risks in unexplored areas can be improved by up to 21% compared with the 3D risk model constructed based on drilled wells only.

Analysis of the Lost Circulation Problem

The well-known “lost circulation” problem refers to the uncontrolled flow of whole mud into a formation. In order to address the problem related to the paucity of available data, in the present study, a model is introduced for the lost-circulation risk sample profile of a drilled well. The model is built taking into account effective data (the Block L). Then, using a three-dimensional geological modeling software, relying on the variation function and sequential Gaussian simulation method, a three-dimensional block lost-circulation risk model is introduced able to provide relevant information for regional analyses.

Understanding caldera degassing from a detailed investigation at Lake Rotoiti, Okataina Volcanic Centre, New Zealand

Volcanic lakes in large silicic caldera volcanoes are an important source of CO2 emissions. However, quantifying CO2 output is challenging due to the lack of observed historical CO2 flux records and the large size of the volcanic and hydrothermal systems. Twenty percent of the surface area of Okataina Caldera, Taupo Volcanic Zone, New Zealand is covered with lakes. Geothermal expressions, predominantly on the Okataina caldera margin, occur in at least six different locations, with surface expressions both on land and under water. Lake Rotoiti is located at the northwest edge of the Okataina Caldera, spans across the Tikitere and Taheke geothermal fields, and has inputs from on-land thermal springs, lake floor hydrothermal vents, and from Lake Rotorua. CO2 flux from Lake Rotoiti was assessed using the accumulation chamber method and three techniques are used to process the data for total CO2 emission of the lake: (1) sequential Gaussian simulation (sGs) method allows quantification of CO2 emission with spatial control, (2) graphical statistical approach (GSA) allows the quantification of CO2 emission from different degassing regimes, and (3) a method based on water chemistry of the lake. We find CO2 is mostly emitting at Tumoana Bay and Central Basin, and the emission rate is 271 ± 38 t d−1 of CO2 (based on sGs). We then added this estimated emission data to the existing CO2 data for individual geothermal systems distributed around the active Okataina volcanic centre and calculate that the entire caldera is emitting at least 1856 t d−1 of CO2. The total emission is dominated by the lakes, where the topography is lower. We discuss the implications of this study in terms of preferential degassing locations and the amount of degassing, in particular for CO2, in an active caldera setting.

Fine-Scale Three-Dimensional Characterization of a Reservoir Comprising Braided River Sand Bodies with a Complex Stacking Pattern in a Cratonic Basin

Abstract The complex stacking pattern of braided river sand bodies in the cratonic basins results in a multilevel and multistage spatial distribution of reservoir units, which increases the difficulty of characterizing the spatial distribution pattern of reservoir parameters. Therefore, when characterizing the spatial regularity of this type of reservoir, it is necessary to consider the stacking pattern of the sand bodies to select the constraints. In this paper, the He 8 section of the Sulige 120 area in the Ordos Basin, China, is taken as an example. First, based on analysis of the development scale and genetic type of sand bodies in the area, four sand body stacking patterns and plan-view distribution patterns are identified. Second, a study on how to improve the accuracy of braided river sand body models is carried out, and a sand body model is established. Finally, a three-dimensional model of reservoir properties is established by using the sequential Gaussian simulation method under the constraint of the phase control model. The model results show that the three-dimensional model of the sand bodies established by the multipoint geostatistical method has better connectivity and is more suitable for constructing sand body models under complex sand body stacking conditions. The training images established by the target body method can clearly identify the boundary of the main channel of the braided river and solve the discontinuity of the sand body distribution. The reservoir model established under the constraint of the sand body model can effectively simulate the nonhomogeneity in the reservoir parameters, which can play a crucial role in accurately predicting the porosity and permeability distribution patterns of reservoirs with complex sand body stacking pattern. The present study provides a new idea for characterizing complexly stacked sand body reservoirs in a cratonic basin.

Distribution of Petrophysical Properties Based on Conceptual Facies Model, Mishrif Reservoir/South of Iraq

A 3D geological model is an essential step to reveal reservoir heterogeneity and reservoir properties distribution. In the present study, a three-dimensional geological model for the Mishrif reservoir was built based on data obtained from seven wells and core data. The methodology includes building a 3D grid and populating it with petrophysical properties such as (facies, porosity, water saturation, and net to gross ratio). The structural model was built based on a base contour map obtained from 2D seismic interpretation along with well tops from seven wells. A simple grid method was used to build the structural framework with 234x278x91 grid cells in the X, Y, and Z directions, respectively, with lengths equal to 150 meters. The total number of grids is (5919732) in the geological model. CPI (computer-processed interpretation) for 7 wells contain (facies, porosity, water saturation, and NTG) was imported to Petrel 2016 software. Facies log was upscaled and distributed along the 3D grid. Truncated Gaussian with trend method was used to distribute the facies taking into account the conceptual facies model of the Mishrif formation. The result shows that the trend of sedimentation suggests a retrogradation pattern from NW to SE. Facies1 (Reservoir), dominated by Limestone brown to light brown, with oil shows has good distribution within the area and thinning towards the NW. The petrophysical properties (porosity, water saturation, NTG, and permeability) were distributed using the Sequential Gaussian Simulation (SIS) method and the facies model as a guide for distribution.
 The results show that petrophysical properties enhanced in the southeast area, representing the reef region compared to the northwest side of the study area. Unit Mishrif B had the highest porosity value and lower water saturation value along the entire field. While the units Mishrif B1, B2, and B3 show a gradual decrease in reservoir properties towards the field's southeast side. The results also show that the conceptual facies model has great benefit in constructing the 3D geological model, reflecting the geological knowledge used to correctly distribute the reservoir properties (porosity and water saturation).

Effect of permeability heterogeneity on the dissolution process during carbon dioxide sequestration in saline aquifers: two-and three-dimensional structures

The convection–diffusion process of carbon dioxide (CO2) dissolution in a saline reservoir is investigated to shed light on the effects of the permeability heterogeneity. Using sequential Gaussian simulation method, random permeability fields in two and three-dimension (2D and 3D) structures are generated. Quantitative (average amount of the dissolved CO2 and dissolution flux) and qualitative (pattern of the dissolved CO2 and velocity streamlines) measurements are used to investigate the results. A 3D structure shows a slightly higher dissolution flux than a 2D structure in the homogeneous condition. Results in the random permeability fields in 2D indicates an increase in the standard deviation of the permeability nodes enhances the dissolution efficiency, fluctuations in CO2 dissolution flux, separation between the different realizations from the same input parameters, and tendency toward more jagged convective fingers’ shape. Furthermore, the distance between the permeability nodes increases the convective fingers’ dissolution efficiency and jagged structure. The degree of freedom in 3D structures results in a higher chance of escaping from the low permeability zones and reduces the interactions between convective fingers in 3D systems. With the same variance and correlation length between permeability nodes, connectivity between high permeable zones in 3D cases are less than that of 2D cases; therefore, 2D realizations overestimate the dissolution flux of real heterogeneous 3D structures, which should be considered carefully.Article HighlightsCO2 sequestration in two and three dimensional heterogeneous saline aquifers are investigated.3D structures in homogeneous conditions show higher dissolution than 2D structures.2D realizations overestimates the dissolution flux over real heterogeneous 3D reservoirs.

Integrated Geologic Modeling of Fault-Block Reservoir: A Case Study of Ss Oil Field

The Ss oil field is found in the Turpan-Hami Basin’s Taipei Sag’s arc structural belt. This reservoir has a complicated character that has a significant impact on reservoir modeling and production prediction. This is a fault-block reservoir with ultralow permeability and low porosity that is divided by 57 faults. A static model was constructed by Petrel software based on reinterpretation of original log and core data and seismic information so as to clarify the spatial distribution of oil and water in the reservoir and to fit the development history of the later simulated reservoir. The integrated geological modeling approach is described in this work using the Ss reservoir as an example. A 3D structural model was built based on the spatial cutting relationship between the layer model and the fault, and the model’s quality was improved by breakpoint data, which more correctly depicted the structural properties of the research area. The lithofacies model was built within the restrictions of sedimentary facies using the sequential Gaussian simulation (SGS) stochastic modeling approach, which is paired with variogram data analysis to achieve the range value. To obtain the porosity and permeability model, the empirical formula of porosity and permeability, the SGS method, and the variation range value was input into the lithofacies model. It is important to note that the input lithofacies and property models have values of the same range. To gain the water saturation model, the distinct S w function formulas of the S 1 ~ S 4 layer derived from the J S w function were fed into the software. The NTG model was created according to the lower limit of porosity, which is 11%. The merging of detailed reservoir description and simulation led to the establishment of the Ss reservoir geological model. In the plane, the scale of the geological model has reached the meter level and decimeter level in the longitudinal direction. It also offers a framework for optimum reservoir modeling for complex fault-block reservoirs. This method improves the accuracy and precision of the model by reflecting the reservoir’s heterogeneity and the oil-water distribution. It could provide more details for future reservoir research such as fine reservoir simulation.

Analysis of spatial and temporal changes of RUSLE-K soil erodibility factor in semi-arid areas in two different periods by conditional simulation

ABSTRACT Soil erosion is the most important soil degradation process threatening semi-arid and arid areas. In this study, the change in the RUSLE-K soil erodibility values due to changed climatic conditions over a 10-year period was used as a tool to investigate soil erosion potential with Sequential Gaussian Simulation (SGS) method in the Mogan catchment in Turkey. For this purpose, soil erodibility values were determined for soil samples taken from the same coordinates in 2000 and 2010, and erosion susceptibility distribution maps were produced. The results showed that even though land use practices remained unchanged, soil erodibility values changed spatially and temporally, with the relationship dependent on climatic factors. More specifically, the effects of decreased precipitation, exacerbated by increased evaporation and more prolonged dry periods, made some areas in the catchment more susceptible to erosion. The vulnerability soils in the catchment increased markedly, with the area classified as subject to ‘very severe erodibility’ increasing more than threefold, from 762 ha to 2477 ha. This study has reinforced the need for ‘minimal disturbance’ land use, supplemented by monitoring that incorporates meteorological and soil test data, to minimize soil erosion and maximize the sustainability of agricultural activities in semi-arid and arid areas.

Combining Sequential Gaussian Simulation and Fractal Analysis for Mapping of Cu Concentration in Haftcheshmeh Porphyry Copper Deposit

In this paper a hybrid approach of Sequential Gaussian Simulation (SGSIM) and Concentration-Volume (C-V) fractal method was applied to boreholes data for classification of major geochemical parameters and associated features with alteration zones of the Haftcheshmeh Cu deposit in NW of Iran. Thereupon, Cu parameters were detected with higher efficiency and lower uncertainty. The purpose is also extended to delineate the alteration zones pertinent to Cu mineralization. Firstly, the most straightforward simulation (SGSIM) was utilized for projecting different lithogeochemical parameters of Cu. Then the C-V fractal model was used to discriminate these parameters by thresholds, obtained from the C-V Log-Log plot. The Fractal based resulting maps of 10 realizations and their E-type indicate their association with potassic alteration that has imposed on porphyry granodiorite. Moreover, these maps illustrate that the boreholes (1, 9, 23, and 31) at about longitudes of 643400 to 643800 are more promising than others. This fact explicitly had been correlated with reality of the studied area and denoted in its primary surface map. The results based on SGSIM, C-V confirmed enhanced mineralization in three-dimensional maps of the Haftcheshmeh deposit as a powerful combined method that can be used to detect the similar ore zones in continuation of the ore roots in adjacent areas.

Static Reservoir Modeling of the Eocene Clastic Reservoirs in the Q-Field, Niger Delta, Nigeria

This work integrates seismic and well log data to establish a 3D reservoir model of the Q-Field, which is a prolific onshore hydrocarbon field situated in the Central Swamp Depobelt of Nigeria. The subsurface modeling focuses on the four principal clastic reservoir intervals of the Agbada Formation (D6200, D7000, D9000 and E2000), which was deposited in a deltaic to fluvio-deltaic system during Eocene. The seismic-based structural modeling inferred an extensional set-up dominated by NW–SE trending normal faults. Reservoirs are sand-dominated and laterally extensive, as interpreted from the 3D facies model. Well log-based petrophysical parameters were up-scaled and distributed stochastically using the Sequential Gaussian Simulation method to generate a 3D reservoir property model. Lateral and vertical heterogeneities of the reservoir properties were inferred from the 3D models. In general, the clastic reservoirs exhibit 18–22% porosity, 62–105 mD permeability, moderate to good net-gross thickness, and 36–74% water saturation. Hydrocarbon accumulation was primarily restricted within the anticlines. Gas-down-to exists in the upper three reservoirs (D6200, D7000 and D9000) at 10,577 ft (1 ft = 0.3048 m), 10,756 ft and 11,389 ft, respectively. Gas–oil and oil–water contacts in the E2000 reservoir were interpreted to be at 11,812 ft and 11,886 ft, respectively. Based on the hydrocarbon distribution, oil and gas-in-place volumes were estimated for all the reservoir intervals. The comprehensive 3D modeling work addressed the spatial distribution of the studied reservoir properties and can be directly useful for planning better the future wells for field development.

3D Reservoir Geological Modeling Algorithm Based on a Deep Feedforward Neural Network: A Case Study of the Delta Reservoir of Upper Urho Formation in the X Area of Karamay, Xinjiang, China

Three-dimensional (3D) reservoir geological modeling is an advanced reservoir characterization method, which runs through the exploration and the development process of oil and gas fields. Reservoir geological modeling is playing an increasingly significant role in determining the distribution, internal configuration, and quality of a reservoir as well. Conventional variogram-based methods such as statistical interpolation and reservoir geological modeling have difficulty characterizing complex reservoir geometries and heterogeneous reservoir properties. Taking advantage of deep feedforward neural networks (DFNNs) in nonlinear fitting, this paper compares the reservoir geological modeling results of different methods on the basis of an existing lithofacies model and seismic data from the X area of Karamay, Xinjiang, China. Adopted reservoir geological modeling methods include conventional sequential Gaussian simulation and DFNN-based reservoir geological modeling method. The constrained data in the experiment mainly include logging data, seismic attribute data, and lithofacies model. Then, based on the facies-controlled well-seismic combined reservoir geological modeling method, this paper explores the application of multioutput DFNN and transfer learning in reservoir geological modeling. The results show that the DFNN-based reservoir geological modeling results are closer to the actual model. In DFNN-based reservoir geological modeling, the facies control effect is obvious, and the simulation results have a higher coincidence rate in a test well experiment. The feasibility of applying multioutput DFNN and transfer learning in reservoir geological modeling provides solutions for further optimization methods, such as solving small-sample problems and improving the modeling efficiency.

Isolated carbonate platform reservoir multiple grouped discrete fracture network modelling

The fracture distribution on isolated carbonate platform is complicate congregation, it is necessary to accurately characterize fracture for oilfield development purpose. Based on the imaging logging fracture interpretation, this study analyzed the fracture grouping condition by sedimentary facies belt, also evaluated corresponding fracture intensity and orientation parameters; the fracture orientation trend for azimuth continuously changes fracture group can be established using the fracture group azimuth parameter; the intensity property calculation of each fracture group was carried out by Sequential Gaussian Simulation method under the seismic curvature volume control; each group’s discrete fracture network calculated from fracture intensity property and the analyzed fracture parameters accurately described the fracture distribution and orientation, the all groups together upscaled fracture property model shows permeability which consistent with with geological concept. This study shows: fracture features significant differentiated in each sedimentary facies belt on isolated carbonate platform, reservoir fracture can be characterized reasonably and accurately by azimuth trend control, thus lay the foundation for the reservoir simulation.

An Integrated quantitative modeling approach for fault-related fractures in tight sandstone reservoirs

Fault-related fractures widely exist in tight sandstone reservoirs, which greatly improve the heterogeneity of reservoir. Unraveling the spatial distribution of fractures is most important for enhancing oil and gas recovery. In this study, an integrated quantitative modeling approach is proposed and applied to the Boxing sag in Jiyang Depression, Bohai Basin, China. From observations of drilled cores and well log interpretations we construct the internal structure of the fault damage zone and establish its geological conceptual model. A single well rock mechanics model is built by applying BP neural network method on rock mechanics experimental data. With the sedimentary facies guided lithofacies model, a three-dimensional heterogeneous rock mechanics model is constructed by integrating three geostatistical methods (i.e. sequential Gaussian simulation method, probability volume modeling method and trend modeling method). This workflow assigns the geological concept and rock mechanics characteristics to the geological volume. Based on the energy conservation principle, the relationship between fracture density and strain energy is derived using different fracture criteria. Based on simulation and calculation results, the fracture density model is established by deterministic modeling. Finally, these properties are incorporated into a DFN model. The model results show that, at the regions near the faults, the fracture development is mainly controlled by faulting. The induced fracture zone is a fracture-intensive area. However, there are few fractures within the fault core zone. The regions far from the faults are mainly controlled by the rock mechanics properties of geological materials, which show zonal characteristics. This model is validated by data from five wells in this region, which shows a strong correlation between predicted fracture density and water cut in produced fluid.

Static reservoir modeling using geostatistics method: a case study of the Sarvak Formation in an offshore oilfield

Static reservoir modeling for optimizing the strategies of hydrocarbon field developments using seismic data and well logs is the focus of this study. The static reservoir modeling is conducted to estimate reservoir properties of the Upper Sarvak formation in one of the offshore oil fields in the Persian Gulf. The Sarvak Formation with middle Cretaceous age is considered as one of the most important productive oil and gas reservoirs in the south and southwest Iran. In this study, we have used seven well logs and 3D seismic data in geostatistical methods for reservoir modeling. The petrophysical parameters such as effective porosity and permeability have been estimated, using the sequential Gaussian simulation method. The evaluation of petrophysical parameters in the reservoir layer shows that the mean effective porosity and permeability are 12.75% and 51.5 mD, respectively. The evaluation of petrophysical parameters from the reservoir characteristic viewpoint demonstrated that a zone called A2 has the best reservoir quality. The well-log validation with core data has been achieved by a cross-validation method. The correlation coefficients derived from the permeability and core data are 86% and 81% for wells w-01 and w-03, respectively, which show that the Sequence Gaussian Simulation is an acceptable method. The result of this study can provide useful information for the development of hydrocarbon exploration studies and reservoir simulation models in this oil field.

Probabilistic estimation of variogram parameters of geotechnical properties with a trend based on Bayesian inference using Markov chain Monte Carlo simulation

ABSTRACT When geotechnical properties show a trend with spatial coordinates, estimation of a variogram model is a challenging task. In the previous studies, the trend-removal method based on ordinary least-squares approach has been commonly used. However, the obtained variogram is biased because the residuals are assumed to be statistically independent. In this study, the ability of Bayesian inference using Markov chain Monte Carlo (MCMC) simulation to estimate the variogram of geotechnical properties with a trend is explored using cone penetration resistance (q c) data of piezocone penetration tests (CPTU). The results show that the Bayesian inference method can estimate variogram parameters and the coefficients of trend function accurately. Based on the posterior variogram models, the predictive uncertainty and total uncertainty bounds are presented using kriging and sequential Gaussian simulation (SGS) methods. 96% validation points lie within the 95% confidence intervals of the total uncertainty based on 200 measurements of q c at NS31. The median and mean SSD of the prediction are 0.34 and 0.87, which is closer to the SSD criterion than the trend-removal method. The model uncertainty of the variograms, the predictive and total uncertainty of prediction all decrease as the sampling density increases at NS31 and NS12.

Groundwater quality assessment of Birjand plain aquifer using kriging estimation and sequential Gaussian simulation methods

The performance of geostatistical estimation methods of ordinary kriging and sequential Gaussian simulation have been investigated in this study to evaluate the qualitative characteristics of groundwater in Birjand plain. For that purpose, 81 water samples were collected from groundwater wells and thirteen hydro-chemical parameters of calcium (Ca), chlorine (Cl), electrical conductivity (EC), bicarbonate (HCO3−), magnesium (Mg), sodium percent (Na%), sodium (Na), sodium absorption ratio (SAR), sulfate (SO42−), total dissolved solids (TDS) and total hardness (TH) were analyzed and interpreted. Variography of the variables was performed after the normalization and experimental variograms were plotted in GS+. The best theoretical models were then fitted to the experimental variograms. Validation of variograms was performed by two methods of cross-validation and residual analysis. Geostatistical estimation maps were then prepared for each groundwater variable. Since the smoothing effect is one of the major drawbacks of ordinary kriging estimations, the sequential Gaussian simulation method was also used to prepare the simulation maps of variables. The simulation results were more reliable than those obtained by ordinary kriging.

Application of 3D Reservoir Geological Model on Es1 Formation, Block Nv32, Shenvsi Oilfield, China

Three-dimensional geological modeling of reservoirs is an essential tool to predict reservoir performance and improve the understanding of reservoir uniqueness in Es1 formation. The paper focuses on the use of petrel software to build three-dimensional reservoir geological model which characterizes and assesses block Nv32 that located in the west of the Shenvsi oilfield in the south of Cangzhou city, Hebei province of China, and has an oil-bearing area of 1.4 km2. This study is depending on integration data from well logs of 22 wells which provided from geology, geophysics, and petrophysics to identify and provide precise depict of the subsurface internal structure and the reservoir heterogeneity. Input data was used to build the structural model, sedimentary facies model, petrophysical properties (porosity, permeability, saturation, and N/G model, and finally to determine the reservoir volume. The lithological facies were simulated using the assigned value method. Moreover, Petrophysical properties (Porosity, permeability, oil saturation and net to gross) were constructed for each zone using the Sequential Gaussian Simulation method to guide the distribution of petrophysical properties of Es1 formation, block Nv32. Statistical analysis of the porosity, permeability, oil saturation and N/G model present that the porosity occurrence distribution is mainly concern between 0.2% - 36.39% of block Nv32 with an average porosity value of 17.5%, permeability between 0.017 mD to 974.8 mD, having an average permeability of 59.44 mD, oil saturation between 0.00 to 0.95 having an average value of 0.22, and N/G is mainly concentrated between 0.01 to 1.00 within an average value of 0.61. This research has indicated the reliability of the three-dimensional model technique as a suitable tool to provide a sufficient understanding of petrophysical distribution. The south-western and north-western indicate that oilfield is very promising an exploratory well should be drilled to find out the thickness and size of the reservoir.

Evaluation of Tailings from a Porphyry Copper Mine based on Joint Simulation of Contaminants

Tailings from porphyry copper mines contain environmentally harmful amounts of elements such as copper, molybdenum, lead and cobalt. Geostatistical simulation of contaminants at unknown locations can be helpful in taking preventive measures for tailings management. In the presence of spatially cross-correlated variables, cosimulation has been the traditional method of evaluation. However, application of cosimulation is cumbersome due to modeling so many auto/cross-variograms at once regarding Cauchy–Schwarz inequality. Moreover, cosimulation may face with an unsolvable system of equations in its algorithm. Then, application of joint simulation using minimum/maximum autocorrelation factors which gives reasonable results would be advantageous. In this study, nine spatially cross-correlated attributes of the Sungun porphyry copper deposit are transformed into orthogonal factors. Each factor is independently simulated by generating one hundred equiprobable realizations through the direct sequential simulation or the sequential Gaussian simulation method. The independent simulations are jointly back-transformed into the original data space using a rotation matrix. Test of the final simulations shows reasonable reproduction of data statistics such as the mean and variance values, cumulative distribution functions, cross-correlations and auto/cross-variograms. For each realization, contamination degrees of the blocks are calculated considering the simulated values of variables, the recovery percentage of contaminants in the tailings and the maximum permissible concentrations of the elements in soils. Blocks are categorized regarding the specified contamination degree thresholds and probabilities of occurrence of low, moderate, high and very high contaminations. Sulfide minerals of copper, antimony, arsenic, zinc, molybdenum and silver consist almost 96% of contaminants. All contaminants are sulfide minerals so that additional bulk flotation of the tailings is suggested for reducing their amounts.