Academic literature on the topic 'Geological model selection'
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Journal articles on the topic "Geological model selection"
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Liu, Yan. "Research of Application of FCE to Project Supervision." Applied Mechanics and Materials 347-350 (August 2013): 214–19. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.214.
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Construct the fuzzy comprehensive evaluation model on geological exploration project supervision index system, and be able to provide reference for geological and mineral exploration relating departments to make decisions. The comprehensive evaluation model is proven to be effective by analyzing some index selection solutions. Apply fuzzy comprehensive evaluation (FCE) method to the selection of geological exploration project supervision indexes, analyze the weights of the various factors which affect the indexes first, then perform fuzzy comprehensive evaluating on the indexes to be determined, and finally classify the evaluated objects and select corresponding indexes according to evaluation results. The method resolves the issues of strong subjectivity and results lacking of quantization which are caused by selecting indexes by simple analogy, and has higher scientificity and practical values.
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Lin, Lai Kuang, Yi Min Xia, Fei He, Qing Song Mao, and Kui Zhang. "Geological Adaptive Cutterhead Selection for EPB Shield Based on BP Neural Network." Applied Mechanics and Materials 607 (July 2014): 118–23. http://dx.doi.org/10.4028/www.scientific.net/amm.607.118.
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In view of complex and fuzziness of geological adaptive cutterhead selection for earth pressure balance (EPB) shield, a cutterhead selection method based on BP neural network is put forward. Considering the structure characteristics of EPB shield cutterhead, typical cutterhead types are classified and summarized based on cutterhead topology structure and number of spokes. After analyzing the determinants of cutterhead selection, one-to-many mapping relation between cutterhead type and geological parameters is put forward, and then core geologic parameters related to cutterhead selection are concluded. The feasibility of using neural network method to choose the cutterhead type is analyzed, and a BP neural network training model for cutterhead selection is set up and tested in testing sample data. The result shows that the selected cutterhead and the construction cutterhead are basically consistent. The feasibility of this method is proved and it can be theoretical basis for the cutterhead structure design which will improve scientific of cutterhead selection.
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Gunnink, J. L., D. Maljers, S. F. van Gessel, A. Menkovic, and H. J. Hummelman. "Digital Geological Model (DGM): a 3D raster model of the subsurface of the Netherlands." Netherlands Journal of Geosciences - Geologie en Mijnbouw 92, no. 1 (April 2013): 33–46. http://dx.doi.org/10.1017/s0016774600000263.
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AbstractA 3D geological raster model has been constructed of the onshore of the Netherlands. The model displays geological units for the upper 500 m in 3D in an internally consistent way. The units are based on the lithostratigraphical classification of the Netherlands. This classification is used to interpret a selection of boreholes from the national subsurface database. Additional geological information regarding faults, the areal extent of each unit and conceptual genetic models have been combined in an automated workflow to interpolate the basal surfaces of each unit on 100 × 100 metre (x,y dimensions) raster cells. The combination of all interpolated basal surfaces results in a 3D Digital Geological Model (DGM) of the subsurface. A measure of uncertainty of each of these surfaces is also given. The automated workflow ensures an easily updatable subsurface model. The outputs are available for end users through www.dinoloket.nl.
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Zhang, An Gang, Zi Fei Fan, and Heng Song. "Evaluation Model of Profile Control Layers' Selection Based on Fuzzy Cluster Analysis." Advanced Materials Research 734-737 (August 2013): 1374–80. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.1374.
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The selection of profile control layers is based on the decision made by single factor at present. Fuzzy cluster analysis based on transitive closure is introduced to establish an evaluation model of profile control layers' selection. Firstly, combining the static geological data and dynamic development data, the evaluation indexes which affect the selection of profile control layers are screened to form the evaluation index system; secondly, according to fuzzy cluster analysis theory, the evaluation index matrix related to profile control layers' selection is dynamically clustered, and the profile control layers are selected on the basis of each category's characteristics of geological and development; finally, the results of profile control layers' selection are examined by the comparison of water injection profile. In addition, the evaluation model is put into practice in a testing well group, and the application shows that the profile control layers selected by the model is reasonable and reliable.
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Bazargan, Hamid, and Mike Christie. "Bayesian model selection for complex geological structures using polynomial chaos proxy." Computational Geosciences 21, no. 3 (March 1, 2017): 533–51. http://dx.doi.org/10.1007/s10596-017-9629-0.
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Abbassi, Bahman, Li-Zhen Cheng, Michel Jébrak, and Daniel Lemire. "3D Geophysical Predictive Modeling by Spectral Feature Subset Selection in Mineral Exploration." Minerals 12, no. 10 (October 14, 2022): 1296. http://dx.doi.org/10.3390/min12101296.
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Several technical challenges are related to data collection, inverse modeling, model fusion, and integrated interpretations in the exploration of geophysics. A fundamental problem in integrated geophysical interpretation is the proper geological understanding of multiple inverted physical property images. Tackling this problem requires high-dimensional techniques for extracting geological information from modeled physical property images. In this study, we developed a 3D statistical tool to extract geological features from inverted physical property models based on a synergy between independent component analysis and continuous wavelet transform. An automated interpretation of multiple 3D geophysical images is also presented through a hybrid spectral feature subset selection (SFSS) algorithm based on a generalized supervised neural network algorithm to rebuild limited geological targets from 3D geophysical images. Our self-proposed algorithm is tested on an Au/Ag epithermal system in British Columbia (Canada), where layered volcano-sedimentary sequences, particularly felsic volcanic rocks, are associated with mineralization. Geophysical images of the epithermal system were obtained from 3D cooperative inversion of aeromagnetic, direct current resistivity, and induced polarization data sets. The recovered cooperative susceptibilities allowed locating a magnetite destructive zone associated with porphyritic intrusions and felsic volcanoes (Au host rocks). The practical implementation of the SFSS algorithm in the study area shows that the proposed spectral learning scheme can efficiently learn the lithotypes and Au grade patterns and makes predictions based on 3D physical property inputs. The SFSS also minimizes the number of extracted spectral features and tries to pick the best representative features for each target learning case. This approach allows interpreters to understand the relevant and irrelevant spectral features in addition to the 3D predictive models. Compared to conventional 3D interpolation methods, the 3D lithology and Au grade models recovered with SFSS add predictive value to the geological understanding of the deposit in places without access to prior geological and borehole information.
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Scheer, Dirk, Wilfried Konrad, Holger Class, Alexander Kissinger, Stefan Knopf, and Vera Noack. "Regional-scale brine migration along vertical pathways due to CO<sub>2</sub> injection – Part 1: The participatory modeling approach." Hydrology and Earth System Sciences 21, no. 6 (June 9, 2017): 2739–50. http://dx.doi.org/10.5194/hess-21-2739-2017.
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Abstract. Saltwater intrusion into potential drinking water aquifers due to the injection of CO2 into deep saline aquifers is one of the potential hazards associated with the geological storage of CO2. Thus, in a site selection process, models for predicting the fate of the displaced brine are required, for example, for a risk assessment or the optimization of pressure management concepts. From the very beginning, this research on brine migration aimed at involving expert and stakeholder knowledge and assessment in simulating the impacts of injecting CO2 into deep saline aquifers by means of a participatory modeling process. The involvement exercise made use of two approaches. First, guideline-based interviews were carried out, aiming at eliciting expert and stakeholder knowledge and assessments of geological structures and mechanisms affecting CO2-induced brine migration. Second, a stakeholder workshop including the World Café format yielded evaluations and judgments of the numerical modeling approach, scenario selection, and preliminary simulation results. The participatory modeling approach gained several results covering brine migration in general, the geological model sketch, scenario development, and the review of the preliminary simulation results. These results were included in revised versions of both the geological model and the numerical model, helping to improve the analysis of regional-scale brine migration along vertical pathways due to CO2 injection.
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Amaya, M., N. Linde, and E. Laloy. "Adaptive sequential Monte Carlo for posterior inference and model selection among complex geological priors." Geophysical Journal International 226, no. 2 (April 26, 2021): 1220–38. http://dx.doi.org/10.1093/gji/ggab170.
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SUMMARY Bayesian model selection enables comparison and ranking of conceptual subsurface models described by spatial prior models, according to the support provided by available geophysical data. Deep generative neural networks can efficiently encode such complex spatial priors, thereby, allowing for a strong model dimensionality reduction that comes at the price of enhanced non-linearity. In this setting, we explore a recent adaptive sequential Monte Carlo (ASMC) approach that builds on annealed importance sampling (AIS); a method that provides both the posterior probability density function (PDF) and the evidence (a central quantity for Bayesian model selection) through a particle approximation. Both techniques are well suited to parallel computation and rely on importance sampling over a sequence of intermediate distributions, linking the prior and the posterior PDF. Each subsequent distribution is approximated by updating the particle weights and states, compared with the previous approximation, using a small pre-defined number of Markov chain Monte Carlo (MCMC) proposal steps. Compared with AIS, the ASMC method adaptively tunes the tempering between neighboring distributions and performs resampling of particles when the variance of the particle weights becomes too large. We evaluate ASMC using two different conceptual models and associated synthetic cross-hole ground penetrating radar tomography data. For the most challenging test case, we find that the ASMC method is faster and more reliable in locating the posterior PDF than state-of-the-art adaptive MCMC. The evidence estimates are found to be robust with respect to the choice of ASMC algorithmic variables and much less sensitive to the model proposal type than MCMC. The variance of the evidence estimates are best estimated by replication of ASMC runs, while approximations based on single runs provide comparable estimates when using a sufficient number of proposal steps in approximating each intermediate distribution.
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Semen, Korchak. "Obtaining initial data for forecast engineering geological model construction." Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal 5 (October 20, 2022): 66–76. http://dx.doi.org/10.21440/0536-1028-2022-5-66-76.
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Objective and relevance. Technologies in mining and geology are intensely developing making the present research particularly relevant. The research considers the problems of obtaining reliable and high-quality data on the degree and nature of rock mass fracturing in a solid mineral deposit. These are fundamental data required to construct a forecast engineering geological model of a deposit. The engineering geological model is usually based on a structural geological model with the addition of various geomechanical parameters used in generally accepted international classifications for rock stability analysis, in particular, RMR (Bieniawski), MRMR (Laubscher), and Q-system (Barton). Methods of research. The authors describe the procedure for obtaining classification geomechanical parameters of a rock mass according to the data from oriented and non-oriented core drilling of special engineering geological (geomechanical) boreholes. Research results. The main factors and conditions for reliable data acquisition are given. Among the most important ones are: economic feasibility and validity of geomechanical boreholes drilling as well as their number; features of the geological and structural composition of a deposit as a condition for borehole location selection; proper organization of the drilling process aimed at obtaining high-quality core material; characteristics of geomechanical description of the core, depending on the rating system of rock mass classification applied. Scope of results. By way of example, deposits with different geological and structural conditions are listed where the described procedure was applied. High-quality and reliable information about the structural and tectonic features of the rock mass allows to create and fill the engineering geological model with data and solve the problems of stability analysis for almost any area of the future mine working.
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Eliawa, Ali i. "Using Geological and Topographic Maps in Site Selection of Solid Waste Disposal." Al-Mukhtar Journal of Sciences 37, no. 1 (March 31, 2022): 29–40. http://dx.doi.org/10.54172/mjsc.v37i1.446.
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Currently, land shortage for solid waste in most urban areas is a significant and growing potential problem. Although some efforts are made to decrease and recover the waste, landfill is still the most common process for waste disposal. Site selection of solid waste dumping in urban areas is a serious subject because of its huge effect on the economy, ecology, and environmental health. Consequently, several criteria must be created because of the difficulty of the parameters to select the process for combination in social, environmental and technical parameters. In this research, the most appropriate sites for locating dumping garbage are determined using the Geographical Information System (GIS) by implementing both methods Boolean logic model and Index overlay model. Based on several objectives, a provided spatial data set consisting of several maps in the form of layers, such as land use, geological distribution, landslides, etc., were used in the modeling process to choose the best site to dump the garbage of Chinchina city that used as a case in this application. The findings show that the Boolean logic model identified only two areas that met the criteria, whereas the Index overlay model identified three important classes through weight; unsuitable, moderate and suitable regions for construction waste disposal.
Dissertations / Theses on the topic "Geological model selection"
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Singh, Harpreet. "Assessing reservoir performance and modeling risk using real options." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5149.
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Reservoir economic performance is based upon future cash flows which can be generated from a reservoir. Future cash flows are a function of hydrocarbon volumetric flow rates which a reservoir can produce, and the market conditions. Both of these functions of future cash flows are associated with uncertainties. There is uncertainty associated in estimates of future hydrocarbon flow rates due to uncertainty in geological model, limited availability and type of data, and the complexities involved in the reservoir modeling process. The second source of uncertainty associated with future cash flows come from changing oil prices, rate of return etc., which are all functions of market dynamics. Robust integration of these two sources of uncertainty, i.e. future hydrocarbon flow rates and market dynamics, in a model to predict cash flows from a reservoir is an essential part of risk assessment, but a difficult task. Current practices to assess a reservoir’s economic performance by using Deterministic Cash Flow (DCF) methods have been unsuccessful in their predictions because of lack in parametric capability to robustly and completely incorporate these both types of uncertainties.
This thesis presents a procedure which accounts for uncertainty in hydrocarbon production forecasts due to incomplete geologic information, and a novel real options methodology to assess the project economics for upstream petroleum industry. The modeling approach entails determining future hydrocarbon production rates due to incomplete geologic information with and without secondary information. The price of hydrocarbons is modeled separately, and the costs to produce them are determined based on market dynamics. A real options methodology is used to assess the effective cash flows from the reservoir, and hence, to determine the project economics. This methodology associates realistic probabilities, which are quantified using the method’s parameters, with benefits and costs. The results from this methodology are compared against the results from DCF methodology to examine if the real options methodology can identify some hidden potential of a reservoir’s performance which DCF might not be able to uncover. This methodology is then applied to various case studies and strategies for planning and decision making.text
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Ranjineh, Khojasteh Enayatollah. "Geostatistical three-dimensional modeling of the subsurface unconsolidated materials in the Göttingen area." Doctoral thesis, 2013. http://hdl.handle.net/11858/00-1735-0000-0001-BB9A-B.
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Das Ziel der vorliegenden Arbeit war die Erstellung eines dreidimensionalen Untergrundmodells der Region Göttingen basierend auf einer geotechnischen Klassifikation der unkosolidierten Sedimente. Die untersuchten Materialen reichen von Lockersedimenten bis hin zu Festgesteinen, werden jedoch in der vorliegenden Arbeit als Boden, Bodenklassen bzw. Bodenkategorien bezeichnet.
Diese Studie evaluiert verschiedene Möglichkeiten durch geostatistische Methoden und Simulationen heterogene Untergründe zu erfassen. Derartige Modellierungen stellen ein fundamentales Hilfswerkzeug u.a. in der Geotechnik, im Bergbau, der Ölprospektion sowie in der Hydrogeologie dar.
Eine detaillierte Modellierung der benötigten kontinuierlichen Parameter wie z. B. der Porosität, der Permeabilität oder hydraulischen Leitfähigkeit des Untergrundes setzt eine exakte Bestimmung der Grenzen von Fazies- und Bodenkategorien voraus. Der Fokus dieser Arbeit liegt auf der dreidimensionalen Modellierung von Lockergesteinen und deren Klassifikation basierend auf entsprechend geostatistisch ermittelten Kennwerten. Als Methoden wurden konventionelle, pixelbasierende sowie übergangswahrscheinlichkeitsbasierende Markov-Ketten Modelle verwendet.
Nach einer generellen statistischen Auswertung der Parameter wird das Vorhandensein bzw. Fehlen einer Bodenkategorie entlang der Bohrlöcher durch Indikatorparameter beschrieben. Der Indikator einer Kategorie eines Probepunkts ist eins wenn die Kategorie vorhanden ist bzw. null wenn sie nicht vorhanden ist. Zwischenstadien können ebenfalls definiert werden. Beispielsweise wird ein Wert von 0.5 definiert falls zwei Kategorien vorhanden sind, der genauen Anteil jedoch nicht näher bekannt ist. Um die stationären Eigenschaften der Indikatorvariablen zu verbessern, werden die initialen Koordinaten in ein neues System, proportional zur Ober- bzw. Unterseite der entsprechenden Modellschicht, transformiert. Im neuen Koordinatenraum werden die entsprechenden Indikatorvariogramme für jede Kategorie für verschiedene Raumrichtungen berechnet. Semi-Variogramme werden in dieser Arbeit, zur besseren Übersicht, ebenfalls als Variogramme bezeichnet.
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Durch ein Indikatorkriging wird die Wahrscheinlichkeit jeder Kategorie an einem Modellknoten berechnet. Basierend auf den berechneten Wahrscheinlichkeiten für die Existenz einer Modellkategorie im vorherigen Schritt wird die wahrscheinlichste Kategorie dem Knoten zugeordnet. Die verwendeten Indikator-Variogramm Modelle und Indikatorkriging Parameter wurden validiert und optimiert. Die Reduktion der Modellknoten und die Auswirkung auf die Präzision des Modells wurden ebenfalls untersucht. Um kleinskalige Variationen der Kategorien auflösen zu können, wurden die entwickelten Methoden angewendet und verglichen. Als Simulationsmethoden wurden "Sequential Indicator Simulation" (SISIM) und der "Transition Probability Markov Chain" (TP/MC) verwendet. Die durchgeführten Studien zeigen, dass die TP/MC Methode generell gute Ergebnisse liefert, insbesondere im Vergleich zur SISIM Methode. Vergleichend werden alternative Methoden für ähnlichen Fragestellungen evaluiert und deren Ineffizienz aufgezeigt.
Eine Verbesserung der TP/MC Methoden wird ebenfalls beschrieben und mit Ergebnissen belegt, sowie weitere Vorschläge zur Modifikation der Methoden gegeben. Basierend auf den Ergebnissen wird zur Anwendung der Methode für ähnliche Fragestellungen geraten. Hierfür werden Simulationsauswahl, Tests und Bewertungsysteme vorgeschlagen sowie weitere Studienschwerpunkte beleuchtet.
Eine computergestützte Nutzung des Verfahrens, die alle Simulationsschritte umfasst, könnte zukünftig entwickelt werden um die Effizienz zu erhöhen.
Die Ergebnisse dieser Studie und nachfolgende Untersuchungen könnten für eine Vielzahl von Fragestellungen im Bergbau, der Erdölindustrie, Geotechnik und Hydrogeologie von Bedeutung sein.
Books on the topic "Geological model selection"
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G, Baier William, and Geological Survey (U.S.), eds. An optimization model for selecting training course locations, U.S. Geological Survey. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 1993.
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G, Baier William, and Geological Survey (U.S.), eds. An optimization model for selecting training course locations, U.S. Geological Survey. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 1993.
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An optimization model for selecting training course locations, U.S. Geological Survey. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 1993.
Find full textBook chapters on the topic "Geological model selection"
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Diederichs, M., T. Lam, M. Jensen, M. Perras, and B. Damjanac. "Influence of model selection, constitutive behavior assignment and parametric sensitivity on tunnel, cavern and pillar EDZ assessment for a long-term deep geological repository." In Rock Engineering and Rock Mechanics: Structures in and on Rock Masses, 1249–54. CRC Press, 2014. http://dx.doi.org/10.1201/b16955-216.
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"Managing Centrarchid Fisheries in Rivers and Streams." In Managing Centrarchid Fisheries in Rivers and Streams, edited by Justin M. Haglund, John Lyons, and Paul Kanehl. American Fisheries Society, 2019. http://dx.doi.org/10.47886/9781934874523.ch1.
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<em> Abstract.</em>—Identifying abiotic variables that influence fish recruitment patterns is crucial to understanding, assessing, and managing populations. Smallmouth Bass <em> Micropterus dolomieu </em>have been sampled from five streams in southwestern Wisconsin since 1989 with the goals of explaining variation and describing patterns of annual age-0 relative abundance. Summer water temperature and stream stage data have been collected annually since 2010 and United States Geological Survey modeled stream temperature and stage data were acquired from 1990–2009. Catch-per-unit-effort (CPUE) of age-0 fish was highly variable within and among streams and ranged from 0 to 48.54 fish/100 m across all streams. Random forest models with stepwise variable selection processes were used to determine the relative importance of stream temperature and stream stage variables in describing variation in CPUE from 2010–2016. July mean water temperature and maximum summer temperature explained 69.7% of the variation in CPUE of age-0 Smallmouth Bass. July mean temperature and maximum summer temperature were positively related with CPUE of age-0 fish from 2010–2016; however, modeled July mean water temperatures and modeled maximum summer temperatures were not significantly correlated with CPUE from 1990–2008. We conclude that caution must be taken when using models to predict CPUE of age-0 Smallmouth Bass from temperature or flow variables, as variability in both recruitment patterns and climatic conditions may reduce model application over longer time frames.
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Makarov, Vitaliy, and Mykola Kaplin. "MODELING THE DEVELOPMENT OF THE GAS INDUSTRY IN UKRAINE." In Priority areas for development of scientific research: domestic and foreign experience. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-049-0-33.
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The subject of the research is the directions of development of the gas industry of Ukraine. The purpose of the study is to develop a mathematical model for calculating the program of development of the country's gas industry to solve the problem of choosing options for commissioning of new natural gas fields and intensification of existing fields. The methods of system analysis, linear programming, comparative analysis and expert evaluations are used in the work. A model for calculating a program for the development of the gas industry is proposed to solve the problem of choosing options for commissioning new natural gas fields and intensifying existing fields. The model is based on representing development options with achievable volumes of annual production increase in integer linear programming problems. New and operating natural gas fields can be presented in the model with statistical information on their distribution by reserves and depths with the corresponding development costs, as well as the dependences of the predicted annual production volume on the measures taken and technologies to improve the efficiency of gas extraction. Model calculations provide a two-stage method for determining the options for the development of the industry. At the first stage, a variety of options are optimized according to the criterion of unit costs per 1,000 m3 of gas produced during the entire program period. The second stage ensures the optimal distribution of the selected options between the periods of the program using the criterion of the production volume and with the limited costs of the previous period for the preparation, prospecting and exploration of deposits. The results of calculating feasible options for the development of the gas production industry based on statistical information on volume, mining and geological and cost indicators of the development of resources and natural gas reserves are presented. The calculations investigated the options for the uniform distribution of investment, as well as their growth from the first stage to the next. For both cases, the priority is set for the selection of fields with large reserves at the same depths. Such a procedure for putting fields into operation is expedient, both from the point of view of the criterion for the optimal functioning of the industry over a long period of time – the unit costs of production, and on the basis of considerations of achieving the highest volumes of extraction in the shortest possible time. In the case of small capital investments in the development of the industry, the model selects small-volume reserves of deposits according to the structure of Ukrainian reserves.
Conference papers on the topic "Geological model selection"
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Grataloup, S., I. Thinon, P. Houel, J. Delmas, A. Dufournet, and P. Renoux. "A 3D Geological Model for Site Selection and Characterization in the Paris Basin." In First EAGE CO2 Geological Storage Workshop. European Association of Geoscientists & Engineers, 2008. http://dx.doi.org/10.3997/2214-4609.20146156.
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Grataloup, S., I. Thinon, P. Houel, J. Delmas, A. Dufournet, and P. Renoux. "A 3D Geological Model for Site Selection and Characterization in the Paris Basin." In First EAGE CO2 Geological Storage Workshop. European Association of Geoscientists & Engineers, 2008. http://dx.doi.org/10.3997/2214-4609.20146186.
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Song, Mengxin, Bingxin Xu, Mei Feng, and Xinxi Fu. "Optimization of Exploration Prospects Based on Ant Colony Algorithm and XGBoost Combined Optimization Model." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207721-ms.
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Abstract Traditional exploration prospect optimization is uncertain due to human factor, the primary reason of that problem is the complex nonlinear relationship between trap quality and related geological factors. Some researchers proposed use artificial neural network (ANN) to solve the problem of the comprehensive geological evaluation of traps, because ANN can describe the nonlinear relationship of multiple geological factors. Considering ANN has some drawbacks, such as it is need lots of parameters for training, and the learning process can not be observed. In this paper we proposed a combined optimization model to accomplish optimization of exploration prospects, and express the affinity order between the prospects and its related geological factors, also can provide the data support for exploration. Based on trap data of an oilfield in Africa, there are 12 geological factors related to trap quality, including trap coefficient, trap depth, trap scale, trap area, Reservoir coefficient, Preservation coefficient, hydrocarbon source coefficient, resources etc.. The ant colony algorithm is used for feature selection, and irrelevant and redundant features are eliminated through multiple iterations, making it suitable for model processing and improving training speed. Based on ant colony algorithm, we get the key parameters for XGBoost model training, namely trap area, reservoir coefficient, preservation coefficient, resource, and the key features are used in XGBoost model for training and prediction. Finally, we compared our prediction results with expert prediction, the error is 0. In this paper, we proposed a combined optimization model based on ant colony algorithm and XGBoost for exploration prospect optimization. We recognized the key geological factors and different characteristic rules for exploration prospect optimization, in the process of optimization, ant colony discards the bad features that interfere with classification and recognition, and retains the features that contribute greatly to classification. In comprehensive geological evaluate of trap, the proposed combined optimization model is suitable for complicated nonlinear geological relationship, and express the affinity order between the prospects, the proposed method can work as an auxiliary way in petroleum exploration, also the proposed method can provide decision support for exploration prospect optimization, and finally can fulfill cost decreasing and benefit increasing.
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Xu, Siqing, Mohamed Baslaib, Aaesha Keebali, Humberto Para, and Ahmed BinAmro. "Potential for Permanent CO2 Geological Storage, an Onshore Abu Dhabi Large Scale Assessment." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/210874-ms.
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Abstract CCS technologies are well established, many ongoing projects have proven to be safe and reliable. Saline aquifers can be leading CO2 permanent storage candidates. A comprehensive multi-discipline study was carried out looking at the potential for CO2 sequestration in deep saline aquifer formations onshore Abu Dhabi. The area of interest is large in scale (regional scale) and consists of multi saline aquifer formations. The study has the added complexity that the same saline aquifer formations may be shared with near-by hydrocarbon exploration and production activities. The objectives were to analyze available geological data, define screening, selection and ranking criteria, identify key constraining factors especially concerning CO2 containment assurance, and arrive at regional storage capacity range estimates. Considerable amount of geological data and studies are available, which can assist with regional Onshore Abu Dhabi saline aquifer formation geological characterization for CO2 storage assessment. The characterization synthesis forms the study basis. A benchmarking was conducted, published screening and selection processes were reviewed. By incorporating key formation geological characteristics, a list of potential formation candidates were generated. A new set of guidelines for large scale (regional saline aquifer) CO2 storage candidate screening is proposed, based on the earlier guidelines set out in DOE/NETL-2017/1844 (2017). Detailed considerations and evaluation during the screening process are presented. A new candidate ranking and selection matrix is proposed. Highest ranked candidates from the ranking and selection exercise were identified. Dedicated regional scale 3D saline aquifer formations static model was constructed, and compositional simulation model developed for CO2 sequestration capturing the key CO2 sequestration processes/mechanisms. Storage capacity estimates were obtained from the dynamic model - the results published separately. Large scale (regional saline aquifer) CO2 storage candidate screening, ranking and selection is a challenging process with many inherent uncertainties. It carries critical importance as the results and recommendations would pave the way for further feasibility or detailed studies and the definition of de- risking activities. It requires multi-discipline input and forms the corner stone for large CO2 storage project development. The study approach, new candidate screening guidelines, ranking and selection matrix may offer discernment for Operators planning similar assessments.
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Mahjour, Seyed Kourosh, Antonio Alberto Souza Santos, Susana Margarida da Graca Santos, and Denis Jose Schiozer. "Selection of Representative Scenarios Using Multiple Simulation Outputs for Robust Well Placement Optimization in Greenfields." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206300-ms.
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Abstract In greenfield projects, robust well placement optimization under different scenarios of uncertainty technically requires hundreds to thousands of evaluations to be processed by a flow simulator. However, the simulation process for so many evaluations can be computationally expensive. Hence, simulation runs are generally applied over a small subset of scenarios called representative scenarios (RS) approximately showing the statistical features of the full ensemble. In this work, we evaluated two workflows for robust well placement optimization using the selection of (1) representative geostatistical realizations (RGR) under geological uncertainties (Workflow A), and (2) representative (simulation) models (RM) under the combination of geological and reservoir (dynamic) uncertainties (Workflow B). In both workflows, an existing RS selection technique was used by measuring the mismatches between the cumulative distribution of multiple simulation outputs from the subset and the full ensemble. We applied the Iterative Discretized Latin Hypercube (IDLHC) to optimize the well placements using the RS sets selected from each workflow and maximizing the expected monetary value (EMV) as the objective function. We evaluated the workflows in terms of (1) representativeness of the RS in different production strategies, (2) quality of the defined robust strategies, and (3) computational costs. To obtain and validate the results, we employed the synthetic UNISIM-II-D-BO benchmark case with uncertain variables and the reference fine- grid model, UNISIM-II-R, which works as a real case. This work investigated the overall impacts of the robust well placement optimization workflows considering uncertain scenarios and application on the reference model. Additionally, we highlighted and evaluated the importance of geological and dynamic uncertainties in the RS selection for efficient robust well placement optimization.
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Van Dam, Matthew H. "Biogeography of hyperdiverse flightless weevils reflects the complex geological history of the Sunda Arc revealed through biogeographic model selection." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.92023.
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Hu, Die, Zhengdong Lei, Stephen Cartwright, Steven Samoil, Siqi Xie, and Zhangxin Chen. "Refracturing Candidate Selection in Tight Oil Reservoirs Using Hybrid Analysis of Data and Physics Based Models." In SPE Canadian Energy Technology Conference. SPE, 2022. http://dx.doi.org/10.2118/208883-ms.
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Abstract Refracturing candidate selection problems can be solved via production statistics, virtual intelligence and type-curve matching, and these methods are mostly developed using data-based models. They unleash great power of data but have not considered the influence of geological distributions in physics-based models. This paper combines the strengths of data and physics based models and proposes a hybrid analysis method to improve and strengthen the current methods. Three criteria, production performance, a completion index and a geological distribution around an offset well, and their sub-criteria are selected to build an evaluation system for refracturing candidate wells. Field data is collected and processed to calculate a completion index and production performance. To quantify a geological distribution around a well, a history-matched reservoir simulation model is required. Besides, a graph theory algorithm, Dijkstra’s shortest path, is used to quantify the influence of geological distributions in 3D reservoir models on wells. An analytic hierarchy process and grey correlation analysis are then used to establish a multi-level evaluation system and determine and rank each individual strategic factor. Finally, datapoints are shown in a 3D coordinate system, and custom defined weights are used to calculate the final ranking of potential refracturing wells. In addition, the hybrid analysis is presented on our self-developed visualization platform. A history-matched reservoir simulation model from the Y284 tight oil reservoir is used as a study case. Eight refractured wells’ data is collected and analyzed. As a grey correlation analysis result, a sub-criteron of productivity performance, relative productivity, ranks the first, followed by cumulative liquid production. Completion and resistance rank third and fourth with a small gap. Based on the analysis results, an evaluation system is built up. 14 refracturing candidate wells are analyzed and ranked using the evaluation system. These wells are displayed in a 3D coordinate system, where x, y and z directions represent three criteria separately. Wells distributed in the first quadrant are regarded as optimum candidates to apply refracturing treatments. Correlations of evaluation factors and increased oil production after refracturing treatment are plotted to validate the method. This study explores how to conduct hybrid analysis in a selection workflow of refracturing candidate wells. Combing visualization, interpretability, robust foundation and understanding of reservoir models with accuracy and efficiency, data-driven artificial intelligence algorithms, the experiences distilled, and insights gained from this project show great potential to apply hybrid analysis as well as modelling in oil and gas industry.
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Yuan, Shuwu, Wei Zhou, Ting Li, Hui Wang, Xuehong Peng, Long Xiao, Xudong Luo, et al. "The Accurate Pore Pressure Prediction with Coupled Geomechanical and Thermodynamics Model." In International Petroleum Technology Conference. IPTC, 2023. http://dx.doi.org/10.2523/iptc-22807-ea.
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Abstract Reservoir pressure and pore pressure coefficient are the key parameters for evaluating the preservation conditions of low permeability reservoirs and selecting different development processed and measures, as well as important input parameters for predicting ground stress. Due to the influence of unique geological characteristics such as ancient structure, current structure and rapid change of burial depth, the pore pressure in reservoir of the Upper Wuerhe Formation in the 53 east block of Junggar Basin has a large lateral change and is influenced by many factors. The conventional pore pressure prediction methods based on longitudinal wave velocity (such as Eaton method) have poor accuracy. Therefore, according to the geological characteristics of the reservoir in this area, based on the simultaneous inversion of P-wave and S-wave data before seismic stack, combined with the changes in formation lithology and the impact of denudation on pore pressure and pore pressure coefficient, this paper takes P-wave, S-wave, lithology, and denudation into account to predict pore pressure and pressure coefficient. The research results show that: ① the introduction of seismic inversion data improves the prediction accuracy and detail richness on the plane; ② the introduction of the lithology change factor improves the stability of the prediction of pressure coefficient in vertical direction; ③ for the area suffering from strong denudation, the introduction of denudation intensity help better predict the pressure coefficient of low pressure wells near the denudated area. The pressure data from more than 10 actual wells proves that the relative error of the prediction results of this method is less than 5%. It is concluded that the established prediction method has small error and high accuracy, and can be used to provide higher quality data support for the subsequent selection of good reservoirs, well location deployment, horizontal stress parameter prediction.
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Timonov, Alexey Vasilievich, Rinat Alfredovich Khabibullin, Nikolay Sergeevich Gurbatov, Arturas Rimo Shabonas, and Alexey Vladimirovich Zhuchkov. "Automated Geosteering Optimization Using Machine Learning." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207364-ms.
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Abstract Geosteering is an important area and its quality determines the efficiency of formation drilling by horizontal wells, which directly affects the project NPV. This paper presents the automated geosteering optimization platform which is based on live well data. The platform implements online corrections of the geological model and forecasts well performance from the target reservoir. The system prepares recommendations of the best reservoir production interval and the direction for horizontal well placements based on reservoir performance analytics. This paper describes the stages of developing a comprehensive system using machine-learning methods, which allows multivariate calculations to refine and predict the geological model. Based on the calculations, a search for the optimal location of a horizontal well to maximize production is carried out. The approach realized in the work takes into account many factors (some specific features of geological structure, history of field development, wells interference, etc.) and can offer optimum horizontal well placement options without performing full-scale or sector hydrodynamic simulation. Machine learning methods (based on decision trees and neural networks) and target function optimization methods are used for geological model refinement and forecasting as well as for selection of optimum interval of well placement. As the result of researches we have developed the complex system including modules of data verification and preprocessing, automatic inter-well correlation, optimization and target interval selection. The system was tested while drilling hydrocarbons in the Western Siberian fields, where the developed approach showed efficiency.
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Rogulina, Alina, Alexey Zaytsev, Leyla Ismailova, Dmitry Kovalev, Klemens Katterbauer, and Alberto Marsala. "Similarity Learning for Well Logs Prediction Using Machine Learning Algorithms." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22067-ms.
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Abstract Determining and predicting reservoir formation properties for newly drilled wells represents a significant challenge for oil and gas companies. Extensive well logs are available only while or after drilling, and thus they bear substantial financial, technical, and operational risks. We propose a new machine learning data-based model for determining well properties similarity and further derive and predict well logs before drilling in a specific geological context. Our model starts with selecting crucial well intervals and aggregation of vital features that determine the petrophysical properties related to particular well layers. Then, a machine-learning algorithm uses this info as input to provide a similarity score between wells. Our fast-to-train nonlinear data-based model is a variant of gradient boosting. We show that this approach can work well in complex scenarios with missing data and inconsistent similarity measures. We compare the modern machine learning algorithms for the evaluation of well similarity models based on aggregated features. The algorithms include gradient boosting and baseline logistic regression models. Our assessment for a real well log dataset via group cross-validation demonstrates that the gradient boosting model pretty accurately identifies well similarity. The receiver operating characteristic quality metric (ROC AUC) is 0.824. The developed similarity learning framework provides a data-driven approach towards estimating well logs for planned and newly drilled wells. Therefore, it allows prediction, improves determination, and can drive an optimal selection of log measurements to be executed in a new well in a specific field / geological context.
Reports on the topic "Geological model selection"
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Jung, Jacob, Michael Guilfoyle, Austin Davis, Christina Saltus, Eric Britzke, and Richard Fischer. Threatened, endangered, and at-risk species for consideration into climate change models in the Northeast. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42143.
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This special report provides a selection process for choosing priority species using the specific focus of high-elevation, forested habitats in the North Atlantic to demonstrate the process. This process includes criteria for choosing invasive species to incorporate into models, given the predicted spread of invasive plant species because of climate change. Discussed in this report are the US Army Corps of Engineers’ Threatened and Endangered Species Team portal, the US Fish and Wildlife Service’s Information for Planning and Consultation Portal, the nonprofit organization Partners in Flight’s watch list, the US Geological Survey’s Biodiversity Information Serving Our Nation model, and NatureServe’s interagency effort Landfire. The data linked this montane habitat with a species of conservation concern, Cartharus bicknelli and the endangered squirrel Glaucomys sabrinus as target species and with Elaeagnus umbellate, Robinia pseudoacacia, Rhamnus cathartica, and Acer planoides as invasive species. Incorporating these links into the climate change framework developed by Davis et al. (2018) will create predictive models for the impacts of climate change on TER-S, which will affect land management decisions in the region.
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Berg, R. C., E. D. McKay, D. A. Keefer, R. A. Bauer, P D Johnstone, B. J. Stiff, A. Pugin, et al. Three-dimensional geologic mapping for transportation planning in central-northern Illinois: Data selection, map construction, and model development. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/299493.
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de Caritat, Patrice, Brent McInnes, and Stephen Rowins. Towards a heavy mineral map of the Australian continent: a feasibility study. Geoscience Australia, 2020. http://dx.doi.org/10.11636/record.2020.031.
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Heavy minerals (HMs) are minerals with a specific gravity greater than 2.9 g/cm3. They are commonly highly resistant to physical and chemical weathering, and therefore persist in sediments as lasting indicators of the (former) presence of the rocks they formed in. The presence/absence of certain HMs, their associations with other HMs, their concentration levels, and the geochemical patterns they form in maps or 3D models can be indicative of geological processes that contributed to their formation. Furthermore trace element and isotopic analyses of HMs have been used to vector to mineralisation or constrain timing of geological processes. The positive role of HMs in mineral exploration is well established in other countries, but comparatively little understood in Australia. Here we present the results of a pilot project that was designed to establish, test and assess a workflow to produce a HM map (or atlas of maps) and dataset for Australia. This would represent a critical step in the ability to detect anomalous HM patterns as it would establish the background HM characteristics (i.e., unrelated to mineralisation). Further the extremely rich dataset produced would be a valuable input into any future machine learning/big data-based prospectivity analysis. The pilot project consisted in selecting ten sites from the National Geochemical Survey of Australia (NGSA) and separating and analysing the HM contents from the 75-430 µm grain-size fraction of the top (0-10 cm depth) sediment samples. A workflow was established and tested based on the density separation of the HM-rich phase by combining a shake table and the use of dense liquids. The automated mineralogy quantification was performed on a TESCAN® Integrated Mineral Analyser (TIMA) that identified and mapped thousands of grains in a matter of minutes for each sample. The results indicated that: (1) the NGSA samples are appropriate for HM analysis; (2) over 40 HMs were effectively identified and quantified using TIMA automated quantitative mineralogy; (3) the resultant HMs’ mineralogy is consistent with the samples’ bulk geochemistry and regional geological setting; and (4) the HM makeup of the NGSA samples varied across the country, as shown by the mineral mounts and preliminary maps. Based on these observations, HM mapping of the continent using NGSA samples will likely result in coherent and interpretable geological patterns relating to bedrock lithology, metamorphic grade, degree of alteration and mineralisation. It could assist in geological investigations especially where outcrop is minimal, challenging to correctly attribute due to extensive weathering, or simply difficult to access. It is believed that a continental-scale HM atlas for Australia could assist in derisking mineral exploration and lead to investment, e.g., via tenement uptake, exploration, discovery and ultimately exploitation. As some HMs are hosts for technology critical elements such as rare earth elements, their systematic and internally consistent quantification and mapping could lead to resource discovery essential for a more sustainable, lower-carbon economy.