Simulation of Geological Models Using Multipoint Histogram

Dynamic simulation and history matching at Ketzin (CO2SINK)

This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models. It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data. Compared with existing geostatistics-based modeling methods, our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called Generative Adversarial Networks (GANs). GANs couple a generator with a discriminator and each uses a deep Convolutional Neural Network (CNN). The networks are trained in an adversarial manner until the generator can create "fake" images that the discriminator cannot distinguish from "real" images. We extend the original GAN approach to 3D geological modeling at the reservoir scale. The GANs are trained using a library of 3D facies models. Once the GANs have been trained, they can generate a variety of geologically realistic facies models constrained by well data interpretations. This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends. The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods, which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.

The development of effective algorithms for the simulation of facies in petroleum systems continues to be limited to techniques relying on linear spatial dependence; Truncated Gaussian Simulation, Plurigaussian Simulation and Sequential Indicator Simulation, in addition to pattern-based techniques based on Multiple Point Statistics. Recent research in the development of new geostatistical techniques for continuous variables has found that the requirement of linear spatial dependence is very rarely satisfied. Datasets from micrometre to kilometre scale have been analysed spatially and, in all cases, significant non-linearity in the spatial dependence structure have been observed. This paper replaces the Gaussian ‘engine’ in Truncated Gaussian Simulation and Plurigaussian Simulation, which requires linear spatial dependence, with a copula-based algorithm that is able to model non-linear dependence structures. The advantage is a facies model that does not contain the symmetric spatial artefacts that characterise Gaussian-based models. In flow simulation terms, this means that the connectivity (and directional connectivity) of different facies within the reservoir architecture to be modelled with greater fidelity, honouring the expected continuities of each of the facies elements. Furthermore, extending recent research by Horning and Bardossy on directional and order asymmetry for continuous variables to consider their application to facies models allows the development of facies-models that are able to represent the directional asymmetry found in sedimentary depositional systems. This allows a better representation of the distal and proximal elements in depositional systems as well as the opportunity to model complex asymmetries associated with meandering channel and estuaries.

Applying network window schema and a simulated annealing technique to optimal VAR planning in large-scale power systems

In practical operational studies, facies stochastic modelling is often performed with traditional geostatistical algorithms such as Sequential Indicator Simulation or Truncated Gaussian Simulation. In this presentation, we will focus on recent improvements of geostatistical techniques used to improve geological realism, and we will stress on the key points for their use in operational context. As a basis of discussion for the workshop, we will briefly address: the use and improvements of Multiple Point Statistics; the generalization of Truncated Gaussian Simulation with efficient Pluri- Gaussian Truncation; and the Boolean models with flexible objects.

Reservoir characterization is a crucial aspect of the oil and gas industry, providing insights into the properties and potential productivity of hydrocarbon reservoirs. Advanced geological modeling techniques have significantly enhanced the accuracy and efficiency of reservoir characterization. These techniques integrate diverse datasets and sophisticated algorithms to create detailed subsurface models that inform exploration, development, and production strategies. One of the primary techniques in advanced geological modeling is seismic interpretation, which involves the analysis of seismic data to map subsurface structures. High-resolution 3D seismic surveys provide detailed images of the subsurface, allowing geologists to identify faults, fractures, and stratigraphic features with greater precision. The integration of seismic inversion techniques converts seismic reflection data into quantitative rock property estimates, such as porosity and fluid saturation, further refining the reservoir model. Geostatistical modeling is another critical technique that improves reservoir characterization. By utilizing statistical methods to analyze spatial variability, geostatistics generates multiple realizations of reservoir properties, capturing the inherent uncertainty and heterogeneity of the subsurface. Techniques such as kriging, co-kriging, and sequential Gaussian simulation are commonly used to interpolate and extrapolate data from well logs and seismic surveys, creating probabilistic models that guide decision-making under uncertainty. Petrophysical analysis plays a significant role in enhancing geological models. Well logs, core samples, and laboratory measurements provide direct information about rock and fluid properties. Petrophysical models are developed to correlate these properties with depth and other geological features. Advances in machine learning and data analytics have further refined petrophysical interpretation by enabling the integration of large datasets and the identification of complex patterns that traditional methods might miss. Another advanced technique is the use of reservoir simulation, which models the flow of fluids within the reservoir over time. Reservoir simulators incorporate geological models, petrophysical data, and production history to predict future reservoir performance under different production scenarios. This dynamic modeling is essential for optimizing recovery strategies, enhancing field development plans, and managing reservoir performance. Multi-scale modeling approaches have also become increasingly important. These approaches integrate data and models across different spatial and temporal scales, from pore-scale petrophysical properties to basin-scale geological structures. By capturing the interactions between processes occurring at different scales, multi-scale models provide a more comprehensive understanding of reservoir behavior and improve the accuracy of predictions. The integration of machine learning and artificial intelligence (AI) has revolutionized reservoir characterization. Machine learning algorithms can process vast amounts of data from various sources, such as seismic surveys, well logs, production data, and geological maps, to identify patterns and relationships that enhance the understanding of reservoir properties. AI-driven techniques, including neural networks and support vector machines, are increasingly used for tasks such as facies classification, porosity prediction, and fracture detection, leading to more accurate and efficient reservoir models. The application of big data analytics in geological modeling has enabled the handling and analysis of massive datasets that were previously unmanageable. Advanced data analytics tools facilitate the integration, processing, and visualization of complex datasets, enhancing the ability to derive actionable insights and improve decision-making in reservoir management. Keywords: Techniques, Reservoir Characterization, Advanced, Geological Modeling, Oil, Gas, Industries

Explicit-implicit-integrated 3-D geological modelling approach: A case study of the Xianyan Demolition Volcano (Fujian, China)

The procedure of the model's generation in FLAC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D</sup> is too simple to establish the complex geologic body's model. So this paper try to study the model's generation algorithms with the theories of computer graphics and explore a software with the language of Visual Basic and Matlab to finish the pretreatment of FLAC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D</sup> 's modeling. The software can establish the complex geologic body's Quasi-3D and 3D model and insert the geologic interfaces of fore model. And some examples are given to verify the software's effect.

Safwan-Zubair area is located in the south –west part of Basrah Province in the south of Iraq, this area is involved within the Dibdibba plain which is considered as a part of Iraqi Western Desert. Dibdibba formation consists mainly from sand and gravel with some cementing materials like silt and clay, especially in the west of Zubair area. A linked simulation-optimization model for obtaining the optimum pumping rate of groundwater is presented in this paper. Processing Modflow for Windows (PMWIN v.5) is selected to simulate the aquifer behavior being studied. This model is integrated with an optimization model which is based on the simulated annealing (SA) technique. The calibrated values of hydraulic conductivity vary over the range (15-150) m/day; while, the calibrated values of specific yield vary over the range (0.125-0.45). The objective function converges to a maximum value of (0.636925E+8 m3/year). The percentage ratio of inactive wells is 10.9 %, many wells in the study area that occupy incorrect positions, these wells must be turn off for obtaining optimum pumping rate in the study area.

Xerox has invented, tested, and implemented a novel class of operations-research-based productivity improvement offerings, marketed as Lean Document Production (LDP), for the $100 billion printing industry in the United States. The software toolkit that enables the optimization of print shops is data-driven and simulation-based. It enables quick modeling of complex print production environments under the cellular production framework. The software toolkit automates several steps of the modeling process by taking declarative inputs from the end user and then automatically generating complex simulation models that are used to determine improved design and operating policies. This chapter describes the addition of another layer of automation consisting of simulation-based optimization using simulated annealing and greedy search techniques that enable the search of a large number of design alternatives in the presence of operational and cost constraints. The greedy search procedure quickly determines an acceptable solution in a web-based online application environment. The simulated annealing technique is more time consuming and is performed offline. The results of the application of this approach to real-world problems are described.

Evaluation of high local groundwater inflow to a rock tunnel by characterization of geological features

Realistic geological modeling can be considered as the first objective in a reservoir characterization workflow. This term describes the process of building static 3-D reservoir models based on available a priori information, geological interpretations and quantitative geological, seismic, and production data. The definition of the best “realistic geological model” to be produced has to be discussed depending on the target objective: to produce a simulation which honors at best the geological parameters and the seismic derived constraints or to produce a simulation which will fit at best the production data. The incorporation of seismic information is a crucial element in reservoir geological modeling. Many recent research works have focused on the introduction in probabilistic models of secondary data characterized by a relationship, often indirect, with the reservoir properties. Due to the different scales of resolution between seismic and geology and to the complex relationship between rock properties and seismic response, a logical process can be to firstly extract geological properties from seismic data and second to constrain the facies simulation using these properties. The extraction of soft geological information from seismic data relies on statistical calibration with well data. Mainly two approaches can be used, either based on supervised segmentation or on estimation techniques. The obtained information consists in variables correlated with the facies distribution. Depending on the available information the variables can be related to the total thickness of the studied stratigraphic unit (leading to 2D constraints as maps) or to small volumes inside the unit (3D constraints). IFP-CG (Institut francais du petrole - Centre de Geostatistique of the Ecole des Mines) have developed an innovative methodology to generate facies proportions grids from well data, sedimentological conceptual models (maps and zonations), and constrained with seismic derived information. The objective of this paper is to present an analysis and evaluation of the real impact of these seismic derived constraints on the reservoir geological model in terms of quantification of uncertainties, heterogeneities distribution, and key geological characteristics. In the geomodelisation workflow, this work takes place when the structural framework of the reservoir is determined and also when the geological conceptual model is defined in terms of interpretation, environment, and layering. Its purpose is to investigate possible variations in parameterization of the scenarios of filling the space with geological properties. Using a real data case, different constraints and different methods of seismic integration are compared. Their impact of seismic in the framework of the truncated gaussian methodology and compared to the impact of the uncertainty on geological data.

This paper presents the work of a multi-disciplinary team of earth scientists and engineers to create a development plan for an offshore heavy oil field while accounting for uncertainties in the geological and reservoir simulation models. The field is located in shallow waters and has oil ranging in viscosities from 20 cp to 200 cp. The depositional environment is a fluvial meandering river system where the sands are relatively thin and disconnected. The field is penetrated by a good spread of appraisal wells, is covered by 3D seismic data, and has extensive measurements of rock-fluid properties and geochemistry analysis. Some of the appraisal wells also have good quality DST and MDT data which was used to constrain the geological model. The workflow is comprised of constructing a geological model using deterministic geobodies identified from interpretive analysis of seismic relative acoustic impedance coupled with stochastically defined sands in areas of poor seismic quality or below the threshold of seismic resolution. The distribution and density of these stochastic sands were tied to well control and guided by stratigraphic layering statistics using a truncated Gaussian simulation technique. Results of both techniques were merged into one final model, embedded into a shale background facies. Various field development scenarios were considered, including inverted five-spot and nine-spot pattern waterfloods, along with the application of horizontal wells. Development scenarios and predictions incorporated uncertainties in wateroil contacts, net-to-gross ratios, oil viscosities, relative permeability, residual oil saturations, injectivity, and skin factors. The use of experimental design techniques and parallel simulation enabled the team to capture the range of uncertainty in subsurface and engineering parameters while keeping computational times manageable. The paper discusses some of the key uncertainties and challenges in bringing this and similar reservoirs to development. Introduction The ability to make quick yet intelligent and value-added decisions to develop new fields has always been of great significance. In situations where the capital expenses and subsurface risk are high, carefully analyzing the inherent uncertainties in the reservoir and how they impact the predicted production becomes a daunting task. The problem is compounded in offshore environments, especially in the presence of heavy oils and disconnected sands, where the margin for error is small. In many cases, realizing the value of a prospect is very time-dependent and resolving uncertainties of complex reservoir systems needs a systematic and efficient approach. This paper presents the case study of the uncertainty analysis workflow for a new offshore heavy oil reservoir for which a development decision had to be made in a short timeframe (a few months). The field is located in shallow waters and is penetrated by 11 exploration/appraisal wells. The depositional environment is predominantly a fluvial meandering river system, where the sands are relatively thin (5 – 15 m in thickness) and disconnected. All the appraisal wells have good quality log data and several attempts were made to tie reservoir zones to 3D seismic attributes.

Analog outcrops are commonly used to develop predictive reservoir models and provide quantitative parameters that describe the architecture and facies distribution of sedimentary deposits at a subseismic scale, all of which aids exploration and production strategies. The focus of this study is to create a detailed geological model that contains realistic reservoir parameters and to apply nonlinear acoustic full-waveform prestack seismic inversion to this model to investigate whether this information can be recovered and to examine which geological features can be resolved by this process.Outcrop data from the fluviodeltaic sequence of the Book Cliffs (Utah) are used for the geological and petrophysical two-dimensional model. Eight depositional environments are populated with average petrophysical reservoir properties adopted from a North Sea field. These units are termed lithotypes here. Synthetic acoustic prestack seismic data are then generated with the help of an algorithm that includes all internal multiples and transmission effects. A nonlinear acoustic full-waveform inversion is then applied to the synthetic data, and two media parameters, compressibility (inversely related to the square of the compressional wave velocity vP) and bulk density, ρ, are recovered at a resolution higher than the shortest wavelength in the data. This is possible because the inversion exploits the nonlinear nature of the relationship between the recorded data and the medium contrast properties. In conventional linear inversion, these details remain masked by the noise caused by the nonlinear effects in the data. Random noise added to the data is rejected by the nonlinear inversion, contributing to improved spatial resolution. The results show that the eight lithotypes can be successfully recovered at a subseismic scale and with a low degree of processing artifacts. This technique can provide a useful basis for more accurate reservoir modeling and field development planning, allowing targeting of smaller reservoir units such as distributary channels and lower shoreface sands.

Uncertainty in geological structural modeling, especially geological corrosion(a kind of karst cave), is a bottleneck that restricts the development and application of geological computer modeling and effect estimation. To solve this issue, a stochastic modeling method based on the random field theory is proposed in comparison with the deterministic geometric modeling method. Then the constraint random field modeling method and the random field modeling method without constrained parameters are compared and analyzed. A case study shows that the novel stochastic simulation method is an effective tool to describe the distribution characteristics of corrosion parameters and reflect the updated geological prospecting information. The influence of geological corrosion on the dam behavior can also be better analyzed by using the stochastic simulation method. At the same time, the unconfined random field ignores the sample location information and may lead to higher variability. Therefore, the constraint random field modeling method can provide a useful reference for the numerical analysis under complex geological conditions.