Geological Rules Research Articles

Structurally Constrained Initial Impedance Modeling for Poststack Seismic Inversion

The establishment of initial subsurface model is a crucial step for seismic inversion. An accurate and reasonable initial model can mitigate the ill-posedness of seismic inversion and improve the quality of inversion result. A common method for building initial model is the well-log data interpolation. However, the traditional well-log interpolation method ignores the structural information of the subsurface, resulting in the constructed initial model lacking geological meaning. We propose a novel structurally constrained modeling method (SCMM) to obtain a geologically reasonable initial impedance model for poststack seismic inversion. Well-log interpolation can be represented as an inverse problem. SCMM constrains the inversion process by using a regularization operator that forces the well-log data to be extended to the entire seismic working area along the subsurface local structural direction. First, we calculate the seismic dip from the poststack seismic profile. Then, we design the structural operator based on the estimated seismic dip information to constrain the interpolation process. Under the framework of inversion, the interpolation objective function can be established by combining the structural operator with the well-log data misfit term, and it can be solved efficiently by the conjugate gradient algorithm. Synthetic and field data tests show that the initial model built by SCMM is more consistent with the geological rules than that built by traditional method, and the poststack impedance inversion using SCMM is better than that using traditional modeling method in terms of convergence property and accuracy of inversion result.

Machine learning assisted history matching for a deepwater lobe system

High exploration costs resulting in sparse datasets and complicated geological structures in deepwater depositional systems make the reservoir characterization extremely difficult. To meet this challenge, rule-based geostatistical subsurface modeling has been developed to fill this data gap with enhanced integration of geological concepts for deepwater reservoirs based on geological rules constrained by this sparse well data. The rule-based model simulates sediment dynamics through depositional rules to calculate reservoir architecture and the associated rock properties distributions. As a result, rule-based models incorporate conceptual and qualitative information, such as temporal deposition sequence and consequent compensational stacking patterns. Integrating quantitative data, such as fluid production history, is a remaining obstacle to the broad application of rule-based subsurface models. We propose a new machine learning assisted history matching workflow for rule-based models. First, multiple rule-based models are calculated as training data for a Generative Adversarial Network (GAN). The successfully trained GAN enables exploration of the latent reservoir manifold with only a small number of numeric values (i.e., latent random vector); this is a massive reduction in the dimensionality of potential subsurface models. The initial ensemble models, generated by the trained GAN with a range of latent random vectors, are coupled with physics-based reservoir flow simulation to obtain production forecasts. Ensemble Kalman filter (EnKF) updates the latent random vectors of the ensemble by minimizing the misfit (i.e., error norm) between the production forecasts and the production observation over time. Our proposed workflow finds an ensemble of optimized reservoir models that honor realistic geological heterogeneity and production history. Besides, as the GAN's latent random vectors are Gaussian distributed, one of the fundamental mathematical assumptions of EnKF, this workflow effectively alleviates possible artifacts (e.g., filter divergence or overshooting) in EnKF. Moreover, this proposed workflow is more computationally efficient than updating the entire high-dimensional reservoir properties and removes the limitation to only Gaussian simulation models with their limited geological realism. The proposed workflow may be expanded to various reservoir depositional settings. • We propose a machine learning assisted history matching workflow for complex deepwater subsurface models. • We apply generative adversarial networks (GAN) to represent the complex subsurface models by latent random vectors. • We update latent random vectors of the subsurface model to match the production history by the ensemble Kalman filter (EnKF). • By combining GAN and EnKF, we can 1) enhance computational efficiency, 2) remove artifacts, and 3) preserve geologic realism.

Modeling extra-deep electromagnetic logs using a deep neural network

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training data set. The data set size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training data set that embraces the geologic rules and geosteering specifics supported by the forward model. We use this data set to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite using a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multilayer synthetic case and a section of a published historical operation from the Goliat field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte Carlo inversion algorithms within geosteering workflows.

Deep Recurrent Neural Networks Approach to Sedimentary Facies Classification Using Well Logs

Determination of lithofacies is one of the most important steps for reservoir characterization. Well log curves are not always sufficient to determine lithology as some times the signals are similar for different lithologies. We believe that the sequence of sedimentary patterns that follows the general geologic rules can be essential to help this disambiguation. This work aims to present a computational system based on deep recurrent neural networks (RNNs) as an effective method to automatically identify lithofacies patterns from well logs. We show that bidirectional long-short-term memory (BiLSTM) RNNs can learn long-term dependencies between time steps of sequence data improving the context available to facies classification. We validated our method by applying it to a real case study from Rio Bonito Formation (Paraná Basin, Brazil) and the proposed method is compared with XGBoost, Random Forest, Naïve Bayes, and support vector machine (SVM) learning approaches. The results indicate that the performance of the proposed method for lithology identification is higher when compared with these other methods.

Adjoint method acceleration protocols for model maturation to update static models with time-lapse reservoir surveillance data

We develop an ensemble model-maturation method that is based on the Randomized Maximum Likelihood (RML) technique and adjoint-based computation of objective function gradients. The new approach is especially relevant for rich data sets with time-lapse information content. The inversion method that solves the model-maturation problem takes advantage of the adjoint-based computation of objective function gradients for a very large number of model parameters at the cost of a forward and a backward (adjoint) simulation. The inversion algorithm calibrates model parameters to arbitrary types of production data including time-lapse reservoir-pressure traces by use of a weighted and regularized objective function. We have also developed a new and effective multigrid preconditioning protocol for accelerated iterative linear solutions of the adjoint-simulation step for models with multiple levels of local grid refinement. The protocol is based on a geometric multigrid (GMG) preconditioning technique. Within the model-maturation workflow, a deep-learning technique is applied to establish links between the mesh-based inversion results (e.g., permeability-multiplier fields) and geologic modeling parameters inside a static model (e.g., object dimensions, etc.). Our workflow integrates the learnings from inversion back into the static model, and thereby, ensures the geologic consistency of the static model while improving the quality of the ensuing dynamic model in terms of honoring production and time-lapse data, and reducing forecast uncertainty. This use of deep learning (DL) to post-process the model-maturation outcome effectively converts the conventional continuous-parameter history-matching result into a discrete tomographic inversion result constrained to geological rules encoded in training images.We demonstrate the practical utilization of the adjoint-based model-maturation method on a large time-lapse reservoir-pressure data set using an ensemble of full-field models from a reservoir case study. The model-maturation technique effectively identifies the permeability modification zones that are consistent with alternative geological interpretations and proposes updates to the static model. Upon these updates, the model not only agrees better with the time-lapse reservoir-pressure data but also better honors the tubing-head pressure as well as production logging data. We also provide computational performance indicators that demonstrate the accelerated convergence characteristics of the new iterative linear solver for adjoint equations.

Loop - Enabling 3D stochastic geological modelling

SummaryLoop is a new open source 3D geological and geophysical modelling platform in full development.The new platform consists of 4 main work packages: Knowledge Management: use of AI techniques for knowledge extraction from literature, maps and reports using geological ontology. Geological rules will be encoded to ensure proper knowledge extraction.Geological Event Management: Loop is a time-aware geological modelling platform and the event manager is capturing topological and time relationship between geological objects and structural eventsForward and inverse structural modelling: we will encode structural geological rules in a time-aware context to account for folds (including overprinting), faults, shear zones, unconformities and intrusions. The modelling is based on probabilistic modelling and allows for the definition of an objective function for geology and quantification of uncertainty via posterior probabilities.Uncertainty characterisation and modelling: using stochastic simulations or the result of Bayesian modelling, Loop allows for characterisation and quantification of 3D uncertainty.

Implicit modeling of complex orebody with constraints of geological rules

Abstract To dynamically update the shape of orebody according to the knowledge of a structural geologist's insight, an approach of orebody implicit modeling from raw drillhole data using the generalized radial basis function interpolant was presented. A variety of constraint rules, including geology trend line, geology constraint line, geology trend surface, geology constraint surface and anisotropy, which can be converted into interpolation constraints, were developed to dynamically control the geology trends. Combined with the interactive tools of constraint rules, this method can avoid the shortcomings of the explicit modeling method based on the contour stitching, such as poor model quality, and is difficult to update dynamically, and simplify the modeling process of orebody. The results of numerical experiments show that the 3D ore body model can be reconstructed quickly, accurately and dynamically by the implicit modeling method.

Lithology identification using well logs: A method by integrating artificial neural networks and sedimentary patterns

Effective identification of lithology using well logs is one of the most important steps for reservoir characterization. A lot of methods have been developed to identify lithology automatically by analyzing the value or patterns of well logs. However, the response characteristics of log curves for some different lithologies are similar and indistinguishable. As a result, we may obtain results which do not follow the general geologic rules if we only use values or patterns of well logs as the criteria to identify lithology. In this paper, we propose an alternative automatic method to identify lithology by integrating both well logs and sedimentary patterns. First, we obtain the probability of lithology by applying the artificial neural networks (ANN) to the well logs. Then, we obtain the vertical combination patterns of different lithologies by applying a probabilistic statistical method to the cores. Finally, we produce an integrated lithology probability by combining the lithology probabilities calculated using ANN and lithology probabilities computed using sedimentary pattern. We validated our method by applying it to a real case study from China. The results indicate that the accuracy of lithology identification using the proposed method is much higher (91%) than that using neural networks method (83%).

Reconstruction of geological surfaces using chance-constrained programming

Geological surface modeling is typically based on seismic data, well data, and models of regional geology. However, structural interpretation of these data is error-prone, especially in the absence of structural morphology information, Existing geological surface models suffer from high levels of uncertainty, which exposes oil and gas exploration and development to additional risk. In this paper, we achieve a reconstruction of the uncertainties associated with a geological surface using chance-constrained programming based on multi-source data. We also quantified the uncertainty of the modeling data and added a disturbance term to the objective function. Finally, we verified the applicability of the method using both synthetic and real fault data. We found that the reconstructed geological models met geological rules and reduced the reconstruction uncertainty.

Three-dimensional Reduced-Complexity Simulation of Fluvio-Deltaic Clastic Stratigraphy

Abstract A novel reduced-complexity approach to 3D forward modeling of siliciclastic stratigraphy is presented for the simulation of erosion, transport, and sedimentation in continental, transitional, and marine depositional domains. The numerical model is based on defining centerlines that connect sediment input points to the shoreline. For each centerline, erosional and depositional surfaces bound depositional domains, and sand and mud proportions are assigned to each domain. The position of each depositional surface follows a set of geologic rules and ensures mass balance with sediment input. The numerical model is tested by simulating the basin-fill architecture of the XES02 laboratory experiment run at the University of Minnesota, which generates stratigraphy mimicking a passive-margin basin fill. Automatic calibration is used to test multiple combinations of uncertain model input parameters to find those that produce scenarios consistent with the experimental stratigraphy. Calibrated models accurately reproduce shoreline and mass-balance centroid migration, marine sediment proportions, and shoreline trajectories measured in the XES02 experiment. The models also provide reasonable approximations of sand distribution. Of interest, falling shoreline trajectories in the experiment and the calibrated models develop coeval to topset aggradation or topset incision depending on rate of base-level fall. The results reported herein validate the numerical approach for simulating sand distribution in an experimental basin, representing a first step towards the application of the numerical model in fluvio-deltaic settings. For field applications, analogue data can be used to independently constrain the geometry of surfaces bounding depositional domains and their composition. The simplicity of the numerical model then enables multiple realizations by quickly varying uncertain boundary conditions, allowing probabilistic predictions in settings with limited data constraints.

Assessing and visualizing uncertainty of 3D geological surfaces using level sets with stochastic motion

For many geoscience applications, prediction requires building complex 3D surface models. Because of such complexity, often only a single model is built, possibly with a small set of variants to represent the uncertainty. Recent advancement in implicit modeling has made the construction of 3D geological models simpler; however, automatic assessment and visualization of uncertainty constrained by input geological rules and data constraints is still an active research topic. In this paper, we propose a new method that directly assesses and visualizes the uncertainty of geological surfaces by the means of stochastic motion. We represent the geological surfaces as the addition of stochastic implicit conceptual models and residual functions subject to the constraints of data and geological age relationships. Two sampling approaches to create the stochastic motion are proposed: Monte Carlo and Markov chain Monte Carlo (McMC). The uncertainty is assessed by independent realizations drawn by Monte Carlo sampling. The uncertainty is visualized by a “smooth” movie of gradually evolving geological surfaces that have the same stationary distribution as Monte Carlo, sampled by Markov chain Monte Carlo (McMC). This idea is integrated into the level set equation. Level sets are an ideal way to represent mathematically complex surfaces without explicit grid representations, thereby having the advantage of avoiding tedious topological computations such as defining the connectivity of a surface. We illustrate this new idea with simple synthetic 3D examples, taking the constraints of data and geological age relationships into consideration. Finally, we illustrate the idea using a synthetic data set from a copper deposit, where dense drillholes constrain an ore body with seven different lithologies. Our method provides a direct assessment and visualization of the uncertainty of 3D geological surfaces.

An Open, Object-Based Framework for Generating Anisotropy in Sedimentary Subsurface Models.

The spatial distribution of hydraulic properties in the subsurface controls groundwater flow and solute transport. However, many approaches to modeling these distributions do not produce geologically realistic results and/or do not model the anisotropy of hydraulic conductivity caused by bedding structures in sedimentary deposits. We have developed a flexible object-based package for simulating hydraulic properties in the subsurface-the Hydrogeological Virtual Realities (HyVR) simulation package. This implements a hierarchical modeling framework that takes into account geological rules about stratigraphic bounding surfaces and the geometry of specific sedimentary structures to generate realistic aquifer models, including full hydraulic-conductivity tensors. The HyVR simulation package can create outputs suitable for standard groundwater modeling tools (e.g., MODFLOW), is written in Python, an open-source programming language, and is openly available at an online repository. This paper presents an overview of the underlying modeling principles and computational methods, as well as an example simulation based on the Macrodispersion Experiment site in Columbus, Mississippi. Our simulation package can currently simulate porous media that mimic geological conceptual models in fluvial depositional environments, and that include fine-scale heterogeneity in distributed hydraulic parameter fields. The simulation results allow qualitative geological conceptual models to be converted into digital subsurface models that can be used in quantitative numerical flow-and-transport simulations, with the aim of improving our understanding of the influence of geological realism on groundwater flow and solute transport.

Quantitative study of generated shale hydrocarbon—a case study of lacustrine shale, Yanchang Formation

ABSTRACTShale hydrocarbon has recently received widespread interest due to its emergence as a type of clean energy. However, calculating the quantity of lacustrine shale hydrocarbon is a challenging issue that the petroleum geologists are currently facing, which results in a poor understanding of shale-hydrocarbon distribution. Using mass conservation law, as well as geochemical and geological rules, this paper tries to establish mathematical models for calculating the quantities of lacustrine shale hydrocarbon. A simple method is also introduced to acquire the target values. The lacustrine shale of the Yanchang Formation in the Ordos Basin is selected to establish the mathematical models and the parameter systems. The method is tested to be computationally efficient and highly practical. The results show that the generated shale gas and shale oil per ton of lacustrine shale of the Yanchang Formation are 1.2 m3 and 0.75 kg, respectively. The lacustrine shale of the Yanchang Formation has a high hydrocarbon potential and is recommended as a favorable target for shale hydrocarbon exploration.

Importance of conceptual geological models in 3D reservoir modelling

Conceptual Geological Models (CGM’s) represent geological knowledge and are traditionally visualized by 2D geologic crosssections. Construction of CGMs is a non-linear and complex process which involves application of geological rules and experience. These models describe essential features of geological situations, illustrate the principal processes of the petroleum system, and provide important information about reservoir characteristics, pressure and fluid flow of the field under study. The CGMs are being extensively used as the key input for 3D reservoir modelling and simulation at different stages of E&P projects. A fully integrated CGM building process consists of at least four stages: construction of one or more structural models, identification of depositional models, construction of sedimentary facies models and finally building of diagenetic facies models. This process requires integration of geological, geochemical, geophysical, and petrophysical data along with information from well testing and production into a comprehensive model describing the physical features of the geologic system. This paper briefly describes the different components of CGM and the model building process. The importance of integrating CGM in the 3D reservoir models has been demonstrated through an example of siliciclastic reservoir. This reservoir has complex internal sedimentary distribution and stratigraphy which leads to high subsurface uncertainties. The study shows that different possible conceptual geologic scenarios provide significantly different Hydrocarbon-In-Place, recoverable resources and production forecasts. Therefore, the successful application of appropriate CGM’s is critical for better 3D reservoir characterization and modelling which enables us to identify and rank the key reservoir uncertainties and assess their impact, establish interdependency of spatial property distribution, make volumetric assessments, plan the number and location of wells including their drainage area, optimize recovery efficiency and obtain reliable production forecasts.

Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA

Quantitative lithofacies modeling is important to understand the depositional and diagenetic history, and hydrocarbon potential of unconventional resources at a regional scale. The complex heterogeneous nature and large data dimensionality of unconventional mudstone reservoirs increase the challenge of lithofacies interpretation by conventional qualitative methods. Quantitative shale lithofacies, which are meaningful, mappable, and predictable at core, well log, and regional scales, can be defined based on mineralogy and Total Organic Carbon (TOC) derived from core analysis and advanced geochemical spectroscopy logs (e.g. Pulsed Neutron Spectroscopy, PNS). However, access to numerous and widespread core samples and geochemical log responses is typically limited by cost and time.We apply different mathematical techniques to ubiquitous conventional well log suites calibrated to rock types, defined by the limited number of wells with high-quality core and geochemical logs. The documented interrelationships between lithofacies and conventional logs are propagated with a quantified degree of accuracy in wells without advanced log or core data. Our study addresses issues of different approaches of quantitative lithofacies classification and prediction techniques from well logs. Various data-driven supervised and unsupervised computational approaches, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Self-Organizing Map (SOM) and Multi-Resolution Graph-based Clustering (MRGC), are applied and compared to reduce uncertainty of propagating single-well based lithofacies analysis, and efficiently understand geological trends.Two different dataset from the Devonian Bakken and Mahantango-Marcellus Shale formations in North America are used, in order to undertake a comparative assessment of computational techniques for lithofacies characterization. Original shale lithofacies, defined from geochemical logs and core data, are used to compare the results of selected supervised and unsupervised computational approaches. The results show that both Bakken and Mahantango-Marcellus shale members are vertically and laterally heterogeneous, but can be classified into at least five mudstone lithofacies, along with calcareous siltstone and limestone lithofacies. SVM works better than other techniques for lithofacies classification and prediction in reduced computational time, no iteration, and with highly repeatable results. Accuracy of lithofacies prediction increases if the algorithms are supervised with geological rules.

Qestimation using modifiedStransform based on pre-stack gathers and its applications on carbonate reservoir

Pre-stack seismic data is acknowledged to be more favorable in estimating Q values since it carries much more valuable information in traveltime and amplitude than post-stack data. However, the spectrum of reflectors can be strongly altered by nearby reflector or side lobes of the wavelet, which thereby degrades the accuracy of Q estimation based on the pre-stack spectral ratio method. To solve this problem, we propose a method based on the modified S-transform (MST) for estimating Q values from pre-stack gathers, in which Q values can be obtained with regression analysis based on the relationship between spectral ratio slope and the square of offset. Through tests on a numerical model, we first prove advantages of this pre-stack spectral ratio method compared to the traditional post-stack method. Besides, it is also shown that application of MST would lead to a much more focused intercept, which is the kernel for the pre-stack method. Therefore, the accuracy of Q estimation using MST is further improved when compared with that of conventional S-transform (ST). Based on this Q estimation method, we apply relevant processing methods (e.g. inverse Q filtering and dynamic Q migration) in practice, in order to improve imaging resolution and gathering quality with better amplitude and phase relationships. Applications on a carbonate reservoir witness remarkable enhancements of the imaging result, in which features of faults and deep strata are more clearly revealed. Moreover, pre-stack common-reflection-point (CRP) gathers obtained by dynamic Q migration well compensate the amplitude loss and correct the phase. Its ultimate pre-stack elastic inversion result better characterizes the geologic rules of complex carbonate reservoir predominated by secondary-storage-space.

Identification methods of coal-bearing source rocks for Yacheng Formation in the western deepwater area of South China Sea

Owing to the fact that the coal-beds are with the characteristics of multi-beds, thin single-bed, rapid lateral changes and deep burial, coal-bearing source rocks are difficult to be identified and predicted, especially in the lower exploration deepwater area. In this paper, a new integrative process utilizing geology and geophysics is proposed for better predicting the distribution of coal-bearing source rocks. Coal-beds were identified by the logging responses of “three higher, three lower, and one expand” and carbargilite were recognized by the characteristics of “four higher and one lower”. Based on the above logical decision, coal-beds and carbargilite can be distinguished automatically by cluster analysis of logging curves in verticality. Within the constraints of well-seismic calibration, the coal-beds group also can be detected in horizontality by the integrated representation of “negative phase, higher Q, lower impedance and lower frequency” within the seismic data. However, the distribution of coal-bearing source rocks utilizing geophysical methodology may do not conform to the geological rules of coal accumulation. And then the main geological controlling factors of coal accumulation are comprehensively analyzed as follows: (1) Paleotopography and tectonic subsidence determine the planar range of terrestrial-marine transitional facies markedly; (2) The relative sea level changes affect the accommodation space and shoreline migration, and limit the vertical range of coal-beds. More specifically, the relationship between the accommodation creation rate and the peat accumulation rate is a fundamental control on coal accumulation. The thickest and most widespread coals form where those two factors reached a state of balance; (3) The supply of autochthonous clasts and the distance between deposition places and paleovegetation accumulated area are the critical factor to form abundant coal, which means that if deposition area is close to paleouplift, there would be sufficient organic matters to form abundant source rocks. The results show that the integrated methods can significantly improve prediction accuracy of coal-bearing source rocks, which is suitable for early exploration of western deepwater area of South China Sea.

An Automatic Modeling Method for Intersected Cross-Sections

According to the intersected cross-sections, an automatic method of constructing geological model was proposed. Firstly, in this method, these cross-sections are divided into some unit cells, and the auxiliary lines are increased and sheared by some geological rules in each unit cell. Finally the geological surfaces are created to form geological entities. This method can be applied in constructing geological entity model automatically, which contains the complex geological phenomena such as Pinch-out, changes and so on. This method also can avoid the modeling process of diverse manual way, raises the modeling speed.

SEAM Update

In September, SEAM Corporation signed a contract with Advanced Geophysical Technology to carry out simulations of 3D land-seismic data acquisition on the SEAM Unconventional Model. This model of shale reservoirs set in the stratigraphic sequence of a typical midcontinent basin was designed by members of SEAM Phase II to represent challenges in the exploration and characterization of unconventional plays with seismic methods. Previous articles in this series have described the geology of the Unconventional Model and its digital construction through work that was carried out on behalf of the consortium largely by BP, using its internal geologic modeling software, including a novel program for simulating the deposition of fine stratigraphic layers controlled by a set of geologic rules (Figure 1; see TLE March 2013, page 346). This article describes the acquisition plan for the simulations and some numerical features of the model.

Modelling of the kaolin deposits and reserve classification challenges of Charentes Basin, France

The kaolinitic clays have been exploited for more than a hundred years, in the western part of the Charentes Basin, France, and belong to a paleo-deltaic network. The recent deposits are relatively richer in alumina in comparison with the older ones. The genesis of the kaolin deposits of the Charentes Basin follows simple geological rules, but their detailed geometry has a great complexity, reinforced by the fact that one must distinguish very different clay qualities. The exploitation of the complex deposits which are buried in the deeper level needs the more powerful tools. The paper aims at analyzing the adequacy of the traditional method used in the exploitations of the kaolin deposits of the Charentes Basin in comparison with another method based on geostatistics to define criteria of selection and classification of reserves.