Sequential Gaussian Simulation Research Articles

Numerical Simulation of Carbon Dioxide–Nitrogen Mixture Dissolution in Water-Saturated Porous Media: Considering Cross-Diffusion Effects

The possibility of impure carbon dioxide (CO2) sequestration can reduce the cost of these projects and facilitate their widespread adoption. Despite this, there are a limited number of studies that address impure CO2 sequestration aspects. In this study, we examine the convection–diffusion process of the CO2–nitrogen (N2) mixture dissolution in water-saturated porous media through numerical simulations. Cross-diffusion values, as the missing parameters in previous studies, are considered here to see the impact of N2 impurity on dissolution trapping in more realistic conditions. Homogeneous porous media are used to examine this impact without side effects from the heterogeneity, and then simulations are extended to heterogeneous porous media, which are a good representative of the real fields. Heterogeneity in the permeability field is generated with sequential Gaussian simulation. Using the averaged dissolved CO2 and dissolution fluxes for each case, we could determine the onset of different dissolution regimes and behaviors of dissolution fluxes in CO2–N2 mixture dissolution processes. The results show that there is a notable difference between the pure cases and impure cases. Additionally, a failure to recognize the changes in the diffusion matrix and cross-diffusion effects can result in significant errors in the dissolution process. At lower temperatures, the N2 impurity decreases the amount and flux of CO2 dissolution; however, at higher temperatures, sequestrating the CO2–N2 mixture would be a more reasonable choice due to enhancing the dissolution behavior and lowering the project costs. The results of the heterogeneous cases indicate that heterogeneity, in most cases, reduces the averaged dissolved CO2, and dissolution flux and impedes the onset of convection. We believe that the results of this study set a basis for future studies regarding the CO2–N2 mixture sequestration in saline aquifers.

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.

Identifying groundwater recharge and discharge zones using geostatistical simulation of hydraulic head and its derivatives

Identifying groundwater flow directions and the locations of recharge and discharge areas is critical for effective groundwater management. Groundwater flow directions, the concavity and the locations of extrema (i.e., minima and maxima) can be assessed using the first and second derivatives of the hydraulic head surface. We developed a geostatistical method to jointly simulate hydraulic head and its first and second derivatives using sequential Gaussian simulation. The derivative values were used to identify regional groundwater flow directions, and the second derivative test was used to probabilistically map the concavity and the locations of extrema in the hydraulic head surface. By comparing the mapped concavity and extrema to known features, it was possible to attribute areas of recharge and discharge to physical features of the system, such as rivers, lakes and geological outcrops. This was applied to Triassic aquifers in the Galilee Basin (Queensland, Australia) to delineate the likely recharge and discharge areas. This provided an objective assessment of likely recharge and discharge zones and their uncertainty, which is an important addition to a region where the hydrogeology has been the subject of much conjecture.

Assessment of resource potential from mine tailings using geostatistical modeling for compositions: A methodology and application to Katherine Mine site, Arizona, USA

The mining industry, in most cases, targets a specific valuable commodity that is present in small quantities within large volumes of extracted material. After milling and processing, most of the extracted material and the effluents are stored as waste (tailings) in impoundments, such as dams or waste dumps, or are backfilled into underground mines. In time, tailing materials may become an issue of environmental and health concern due to the hazardous elements, ions, and oxides contained within the waste material. In addition, handling and storage of such waste in dams may pose the risk of dam failure with catastrophic consequences to nature and nearby communities. On the other hand, tailings may offer potential as secondary sources of critical elements (CEs), including rare earth elements (REEs), which may have been overlooked during primary production and processing. Therefore, treating mine tailings as a resource has economic and environmental benefits by reducing the waste from new and historical mine sites through remining. One of the critical steps for taking advantage of these benefits is to spatially quantify the resources and the pollutants, which require the application of adequate data analysis and modeling methods, often to compositional geochemical data. Utilizing adequate methods is especially important for correctly quantifying resource potential, as the quantities will often be at low concentrations.This work presents quantification of resource potential (Au, Ag, Cu, Zn, Pb) and elements of environmental concern (Hg and As) from the tailings of a historic mine site, Katherine Mine, AZ, USA. Data reported by the U.S. Bureau of Mines (USBM) after extensive field campaigns in the 1990s, including sampling from tailing impoundment and surrounding areas for geochemical characterization and geophysical surveys, were used. First, compositional data (CoDa) analysis was employed to explore associations of sampling locations, geochemical parts, and the clustering of samples. Next, sequential Gaussian simulation (SGSIM) was applied to samples that showed a genetic link to tailing material after isometric log-ratio transformation (ilr) and mix/max autocorrelation factor (MAF) transformation for spatial modeling and uncertainty evaluation. Geostatistical results revealed spatial variability of concentrations within the tailing area. Uncertainty evaluation based on realizations indicated that Cu (14.27–20.01 t), Zn (44.23–76.23 t), and Pb (22.56–38.28 t) are the most abundant elements within a 5 %–95 % interval, followed by Ag and Au (~5.3 and 0.18 t, at 50th percentile), respectively. Of the elements of health concern, As was found to be ~4.8 t (50th percentile) in the tailing area. The work also showed that ~0.51 t As, 0.005 t Hg, 0.020 t of Au, and 0.62 t of Ag were carried to Lake Mohave by an ephemeral stream called Katherine Wash, which transects the tailings.

System response of an interlayered deposit with a localized graben deformation in the Northridge earthquake

The Wynne Avenue site in the California San Fernando Valley was analyzed using two-dimensional (2D) nonlinear dynamic analyses (NDAs) with geostatistical subsurface modeling to interpret key mechanisms contributing to a 12-m-wide graben deformation with vertical offsets of 10–20 cm in the 1994 Northridge earthquake. In-situ data from borings and cone penetration tests (CPTs), joined with geological interpretations of the distal alluvial fan deposition, delineate interlayered silty sand lenses within a typically fine-grained soil stratigraphy. The NDAs used the PM4Sand and PM4Silt constitutive models with the FLAC finite difference program. NDAs with uniform properties for distinct soil zones closely reproduced the observed graben. However, stochastic NDAs, modeled with sequential Gaussian simulations (SGS) for conditional random field realizations of critical zones, often obscured the expected graben. The ability of the stochastic NDAs to model the graben was further reduced when SGS was performed collectively over two distinct soil zones. The NDAs provide new insights on the causal mechanisms of liquefaction-induced ground oscillations and lurching, that were not easily deduced from common one-dimensional (1D) liquefaction vulnerability indices (LVIs) and Newmark sliding-block analyses. This case study demonstrates the capabilities of system-level NDAs for modeling localized ground deformation patterns, but also highlights cautionary nuances for their implementation with data-driven stochastic subsurface modeling.

Assessing the spatial variability of saturated soil hydraulic conductivity at the watershed scale using the sequential Gaussian co-simulation method

Accurate spatial characterization of saturated soil hydraulic conductivity (KSat) is vital for modeling hydrological processes. An effective representation of KSat by field sampling is very difficult on a watershed scale, due the heterogeneity of landscape characteristics for wide land surfaces. To improve the estimation of spatial distributions of KSat at this scale, Sequential Gaussian Simulation (SGS) and Sequential Gaussian Co-Simulation (SGCS) algorithms were applied. The study was conducted in a 70 ha watershed, with 178 sampling points. In addition to KSat the database is composed of Bulk Density (BD), Total Porosity (TP), Macroporosity (Mac), Microporosity (Mic) and Organic Carbon (OC). The Land Use (LU) was also quantified as a regionalized variable. A principal component analysis generated additional two regionalized variables (PC1 and PC2). The simulations were evaluated by faithfully reproducing the stochastic and spatial characteristics of the KSat original data. To estimate the KSat, a univariate SGS was run first. The reproduction of the KSat histograms was satisfactory, while the semivariograms of the simulated fields underestimated the sill. The SGCS was run using five different secondary variables: BD; Mac; LU; PC1 and PC2. All SGCS satisfactorily represented the original KSat histogram, with a slight overestimation for some simulated fields. The semivariogram was very well reproduced for SGCS, being an indication that the process was quite efficient for all secondary variables. The smallest uncertainties were identified for the SGCS that used BD and Mac as secondary variables while the greatest uncertainties were associated with the SGS. The results of this study demonstrate that soil hydrological attributes as BD and Mac are a reliable auxiliary dataset to improve the spatial assessment of KSat by SGCS at watershed scale.

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.

Building 3D geological model using non-uniform gridding for Mishrif reservoir in Garraf oilfield

Mishrif formation is the main carbonate reservoir in southern Iraq, which is extremely heterogeneous and require accurate geological description for optimal oil field development. This research is associated with developing a representative 3D static model that conserves geological elements and relieves the uncertainty of reservoir properties and volumetric estimates. The interpreted log data and core reports were selected to capture all structural, stratigraphical and petrophysical properties. The reservoir is discretized with irregular cells using the tartan grid, concentrating their accuracy in important areas. A sequential Gaussian simulation algorithm distributed the petrophysical properties for all reservoir units. The structural model showed that Mishrif reservoir in Garraf oil field has a domal structure with two main producing reservoirs (M1.2 and L1.2). The petrophysical model results present that unit L1.2 has high-quality properties with average porosity of 0.24 and water saturation of 0.17 and less than in unit M1.2, where the average water saturation and porosity are 0.47 and 0.185, respectively. The estimated oil in place with the volumetric method was 901 Million standard cubic meters, most of which accumulated in unit L1.2. This study was applied to reappraise the reserves of Mishrif reservoir in Garraf oil field using irregular gridding.

Application of Volume Uncertainty for Resource Classification: A Case Study on the Rondon Do Pará Bauxite Deposit, Brazil

This study illustrates the application of conditional simulations to calculate the uncertainty associated with the thickness of bauxite ores. The bauxite deposit of Rondon do Pará in northern Pará State, Brazil, is characterized by a well-defined lateritic profile, with the ore being composed of two sequential horizons: massive bauxite and ferruginous bauxite. This study used ore thickness data from 1.005 drillholes with different grid spacing. Drillhole intervals of both types of bauxite ore were accumulated, converting the database from 3D to 2D. Sequential Gaussian simulation produced probability maps calculated from certain confidence intervals, which permits obtaining the uncertainty associated with estimates in thickness. Results show that in portions with the same regular drillhole spacing there are different ranges of uncertainty and variability, which could be useful to support resource classification, associating different confidence intervals to resource classes. This analysis could also guide the drilling program for resource conversion in order to optimize costs, indicating areas where there is greater uncertainty and would need to be densified. The incorporation of this information into the resource model could be very helpful for supporting subsequent studies of economic evaluation and risk analyses with respect to this type of deposit or similarly in mineral exploration.

An advanced workflow to compress the uncertainties of stochastic distribution of Bahariya reservoir properties using 3D static modeling: An example from Heba Oil Fields, Western Desert, Egypt

The main objective of this paper is to construct a static model that compress the uncertainties of the stochastic distribution of the reservoir properties of the Bahariya Formation in Heba field, at the northeastern portion of the Western Desert. This model has been constructed through the integration of the interpretations of the eighteen 2D seismic sections and the analysis of well logs data for four wells (HEBA 300X, E.BAH-E−1X, E.BAH-D-1X, and HEBA 10X) drilled in the study area. This set of data was implemented in a harmonic workflow. Structural framework was the first step created on the basis of the seismic and well log interpretations. Model zonation was mainly managed by the marine flooding events took place during the Cenomanian period. The trapping faults position uncertainty has been compressed through the tying of the seismic profiles with the identified fault cuts in the well data. Effective porosity spectrum was broke up into three reservoir qualities. The results showed heterogeneous facies qualities for oil production in specific five zones in the topmost part of the Bahariya Formation. The effective porosity model was generated stochastically considering the normal distribution for each reservoir quality. Water saturation was distributed by two methods; 1) Sequential Gaussian Simulation that was co-simulated by porosity model. 2) Log-based saturation height function for each reservoir quality. This methodology provided as accurate as possible estimates for the volume calculation by quantifying the sensitivity of the important parameters such as oil contact. Additionally, the model was prepared to be used as a front end for dynamic simulation.

Improving the 3D facies model with the seismic-derived log volumes: a case study from the Asmari Formation in the Hendijan Field, southwest Iran

Abstract The prolific Oligocene-Miocene Asmari Formation is the primary reservoir rock hosting important hydrocarbon resources in Iran and consists of fluvial/deltaic siliciclastic and shallow marine carbonate rocks in the Hendijan Field. Due to the significant facies variability of the formation, the presence and quality of the reservoir pose a significant uncertainty in the characterization of the reservoir. This study compares two facies models, one based on well logs only and the second based on estimated facies volumes as a secondary variable in facies modelling. The petrophysical evaluation with microscopic thin sections and electrofacies analysis were used to classify the facies and determine the reservoir quality. As a result, the Ghar Member was identified as a highly porous interval, while the lower part of the Asmari Formation is characterized by tight facies. A sequential Gaussian simulation (SIS) algorithm was used to build the 3D facies model on the basis of the well logs. Acoustic impedance, shear impedance and density (derived from pre-stack inversion) were used as inputs to an artificial neural network to generate acoustic and density log volumes. Using electrofacies cut-offs, facies volume was constructed and used as a secondary variable to improve the initial facies model. The final facies model was compared with the blind well to check the validity of the prediction and satisfactory results were obtained. Since the values are present in all the cells of the reservoir (the traditional facies model only has values for the well location), the estimated facies volume is an accurate variable in the prediction of the facies model for the Asmari reservoir and for this reason the secondary facies model is more reliable than the primary one.

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).

Accuracy and uncertainty of geostatistical models versus machine learning for digital mapping of soil calcium and potassium.

Accuracy and uncertainty of models used for digital soil mapping are important for assessing confidence of predictions and reliable land use planning and management. In this study, two approaches of geostatistical (spatial) and machine learning (ML) models were evaluated forpredictivemapping of soil calcium (Ca) and potassium (K). Two spatial models including empirical Bayesian kriging (EBK) and sequential Gaussian simulation (SGS) were compared with machine learning models: Cubist, random forest (RF) and support vector machine (SVM) in terms of their accuracy and uncertainty for mapping soil Ca and K. The study area is in Nowley, New South Wales, Australia, with an area of 2083ha and a variety of soil types and farming systems. For the models training process, 240 soil samples data and for validation 102 independent samples data were used. For accuracy assessment R2, root mean square error (RMSE), concordance and bias and for uncertainty assessment confidence limits were investigated. Also, in order to compare the outcomes for the two soil properties with different measurement units, mean absolute percentage error (MAPE) and relative uncertainty (RU) as accuracy and uncertainty measures, respectively, were evaluated. Results showed that for K map SGS had the highest R2 (0.74) and lowest RMSE (1.96), followed by EBK with R2 = 0.72 and RMSE = 2.02. For Ca map, EBK model showed the highest accuracy (R2 = 0.46; RMSE = 3.21), followed by SVM and SGS with comparable accuracies. Comparing the two soil properties, Ca map showed higher MAPE and RU, compared to K map. The lowest MAPE was obtained for EBK model (for K = 39) and SGS model (for K = 44). Also, the lowest RU values were found for EBK and SGS models. Among the ML models, SVM showed lower sensitivity to higher variance in data input. In general, the spatial models outperformed the ML models with regard to both accuracy and uncertainty. An additional conclusion is that considering the data variance in the two soil properties, geostatistical models with lower RU and MAPE were relatively less susceptible to data variance, compared to ML models.

Geology-driven modeling: A new probabilistic approach for incorporating uncertain geological interpretations in 3D geological modeling

Combining different sources of information about the subsurface is an inherent challenge in the process of making realistic geological and hydrostratigraphic models. Often the available geological and hydrological data from boreholes or outcrops are sparse and modeling is supplemented spatially with geophysical data to obtain a better understanding of the 3D lithological, structural, and hydrological relations in the study area. In traditional geological modeling, the modeler combines all this information during modeling and consider several factors like e.g., distance to neighboring data, consistency between different information, data uncertainty and geological environment when assigning uncertainties to the interpretation points. However, the assigned uncertainty is subjective and can only be communicated qualitatively. The benefit of a probabilistic model is that it enables a more quantifiable approach to subsurface modeling, but probabilistic models are usually difficult to set up, computationally demanding as well as difficult to interpret for the geologist and decision makers. Moreover, there is little tradition for including geological knowledge/information directly in probabilistic approaches. In the following, we utilize the interpretations from a traditional manually interpreted (cognitive) 3D hydrostratigraphic layer model as input for a probabilistic model. A realization of the subsurface is created from a geological or hydrostratigraphic model by geostatistical simulation of each interpreted layer based on the geologist's interpretation points with corresponding uncertainties. By compiling all the simulated layers, a 3D structural model is obtained. By studying a sample of such 3D realizations, the interpretation uncertainty in the cognitive structural model can be derived. We name this methodology geology-driven modeling (GDM) as it is based on geological interpreted data rather than the geophysical data directly. The methodology is tested using sequential Gaussian simulation on a cognitive hydrostratigraphic model from Denmark. Our results show that GDM successfully allows transforming the static cognitive model into a full probabilistic model and enables the uncertainties to be communicated to further modeling or decisionmakers. The proposed methodology allows updating pre-existing 3D geological and hydrostratigraphic layer models in a geologically intuitive stochastic framework or be directly incorporated into the current modeling framework.

Data assimilation for surface wave method by ensemble Kalman filter with random field modeling

AbstractThe ensemble Kalman filter (EnKF) was used to estimate the spatial distribution of the Young's modulus of a model of an earth‐fill dam by assimilating the travel time to the first arrival of the surface waves. By the ensemble data assimilation, the measured data from a geophysical exploration was applied to simultaneously estimate the geotechnical properties and evaluate the uncertainties. Swedish weight sounding (SWS) test results were employed as the prior information to generate the initial ensemble through the sequential Gaussian simulation (sGs). In the experiments of assimilation, it was shown that the reproducibility of the parameter field is enhanced by this initial ensemble generation method, and that the uncertainties of the identified parameters can be reduced by the assimilation.

Preliminary geological mapping with convolution neural network using statistical data augmentation on a 3D model

Airborne magnetic data are commonly processed and interpreted by geologists to produce preliminary geological maps of unexplored areas. Machine learning can partly fulfill this task rapidly and objectively as convolutional neural networks applied to image segmentation showed promising results in comparable applications. As these algorithms require a large and high-quality dataset to be trained, we developed an innovative geostatistical data augmentation workflow that uses a 3D conceptual lithological and magnetic susceptibility model as input. The workflow uses soft-constrained Multi-Point Statistics to create equiprobable synthetic 3D geological models and Sequential Gaussian Simulation to populate these models with a geologically meaningful magnetic susceptibility spatial distribution. Then, geophysical forward modeling is computed to get the airborne magnetic responses of the synthetic models, which are associated with their counterpart synthetic geological maps. We applied this workflow on a 3D model of the Malartic Mine area to obtain a large synthetic airborne magnetic and counterpart geological map dataset. Then, a Gated Shape Convolutional Network is trained on this dataset to perform a preliminary geological mapping. A semi-supervised approach using clustering on the feature maps from the trained network is also implemented. Testing the trained network on synthetic data and remote areas of similar geological context shows that the methodology is suitable for producing preliminary geological maps using airborne magnetic data. Notably, the clustering module shows a precise and adaptive segmentation of the magnetic anomalies into pertinent preliminary geological maps. The quality of the results empirically validates our data augmentation method. Our method allows producing geological maps by training a convolutional neural network using an area where a detailed geological and petrophysical 3D model exists and then permits applying the pre-trained model in new areas of the same geological context, where only airborne magnetic data is available.

Risk-based methodology to optimize geotechnical site investigations in tunnel projects

The scope of geotechnical investigations in tunnel projects is generally driven by the allocated resources rather than the expected variability in the ground conditions. Uncertainties in ground conditions may lead to poor decisions in project planning and active risk management. This paper presents a systematic and rational methodology to identify priority locations of additional geotechnical investigations for soft ground tunneling applications based on tunnel risks, site conditions, and project-related constraints. The methodology is applied to the cutter tool wear risk and quantifies the uncertainty in soil abrasivity index (SAI), the relevant geotechnical parameter. Preliminary geotechnical investigation data and site conditions from an actual tunnel project in an urban environment are used to illustrate the proposed methodology and demonstrate its effectiveness. Pluri-Gaussian simulation and sequential Gaussian simulation are used to characterize the 3D spatial variability in soil units and soil abrasivity, respectively, along the tunnel alignment. The study integrates geospatial assessments of SAI uncertainty and consequences of tool wear to develop an R index map that delineates the impact of uncertainty in tool wear rate. Project constraints of drilling accessibility and budget are incorporated in the R index map to find locations of additional geotechnical investigations. The study simulates a virtual sampling of additional boreholes and quantifies the reduction in uncertainty in tool wear rate and tunnel boring machine (TBM) intervention locations. Additional investigation at priority locations is found to reduce cutterhead intervention location uncertainty by approximately 90 rings (160 m). Further, integrating the uncertainty in tool travel distance is found to influence the cutterhead intervention location uncertainty by approximately 40 rings.

Enhancing Characterization of Reservoirs Within the Niger Delta Basin Using Integration of Seismic and Well Logs

This research utilized well logs and seismic to unravel the reservoir architecture of a field within the offshore depolbelt of Niger Delta. Gamma ray and resistivity log responses were used for lithology and hydrocarbon reservoirs identifications. The principle of similarity in log signatures was used for correlation across the three studied wells. Pertinent reservoir parameters such as shale volume, effective porosity, permeability, hydrocarbon saturation and water saturation were calculated using well-established equations consistent with the geology. Seismic structural interpretation that will give insight to deformation of the field involves the identification and mapping of faults and horizons from seismic sections. By integrating information from seismic and wells, 3D geological modelling was done using geostatistical methods of Sequential Gaussian Simulation and Indicator. The general stratigraphy of the studied field comprises of intercalation of sands and shales which is typical of Agbada formation. Three reservoirs designated as AA, BB and CC were identified and mapped. Only reservoir CC was laterally continuous across the three wells. Reservoir AA has average shale volume of 13%, effective porosity 26%, hydrocarbon saturation 46% and permeability 1328 mD. Reservoir BB has shale volume of 10%, effective porosity 28%, hydrocarbon saturation 43% and permeability 1616 mD. Reservoir CC has shale volume of 21%, effective porosity 20%, hydrocarbon saturation 89% and permeability 580 mD. Twenty faults comprising of both major and minor faults were mapped. The faults mapped were majorly normal faults. The result of the seismic structural interpretation shows that the probable structures harboring oil and gas are a combination of four-way closure and fault assisted anticlinal structures. The modelling of facies and petrophysical parameters into maps aided the understanding of parameter’s influence on oil and gas production. Reservoir CC has oil and gas in commercial quantities with 2520 MMcf and 622Mbbls respectively. The result shows the reservoir has high performance system and is therefore recommended for development and production. The resulting models will aid placement of production wells and monitoring of future performance of the reservoirs.

Lithology prediction method of coal-bearing reservoir based on stochastic seismic inversion and Bayesian classification: a case study on Ordos Basin

Abstract The Upper Palaeozoic coal measures in the Ordos Basin are rich in unconventional natural gas resources; however, the reservoir heterogeneity is relatively strong, which majorly restricts the accuracy of lithology prediction. Stochastic seismic inversion can synthesize the geological–geophysical information and establish high-resolution reservoir models based on geostatistical theory to obtain good inversion results; moreover, it can characterize reservoir lithology and fluid-bearing properties. The present study aimed to propose a high-precision lithology classification method for coal-bearing strata in the Ordos Basin, using Bayesian classification and stochastic seismic inversion. Initially, the reservoir geological model was established on the basis of the sequential Gaussian simulation algorithm, and stochastic seismic inversion was performed in combination with rock physics analysis, thereby obtaining a high-resolution elastic parameter data volume. Thereafter, the probability density function (PDF) and the probability density confusion matrix (PDF confusion matrix) were introduced to quantitatively analyse the ability of sensitive elastic parameters to distinguish lithology. Eventually, a logging lithology-fluid classification template was established based on the Bayesian classification technology; furthermore, a 3D lithology-fluid prediction was completed in combination with the inversion results. The prediction results are in good accordance with the logging data, which verifies the feasibility and effectiveness of the method.