Academic literature on the topic 'Seismic zones classifications'
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Journal articles on the topic "Seismic zones classifications"
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Petersen, Mark D., Tousson R. Toppozada, Tianqing Cao, Chris H. Cramer, Michael S. Reichle, and William A. Bryant. "Active Fault Near-Source Zones within and Bordering the State of California for the 1997 Uniform Building Code." Earthquake Spectra 16, no. 1 (February 2000): 69–83. http://dx.doi.org/10.1193/1.1586083.
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The fault sources in the Project 97 probabilistic seismic hazard maps for the state of California were used to construct maps for defining near-source seismic coefficients, Na and Nv, incorporated in the 1997 Uniform Building Code (ICBO 1997). The near-source factors are based on the distance from a known active fault that is classified as either Type A or Type B. To determine the near-source factor, four pieces of geologic information are required: (1) recognizing a fault and determining whether or not the fault has been active during the Holocene, (2) identifying the location of the fault at or beneath the ground surface, (3) estimating the slip rate of the fault, and (4) estimating the maximum earthquake magnitude for each fault segment. This paper describes the information used to produce the fault classifications and distances.
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Lehocki, Ivan, Per Avseth, and Nazmul Haque Mondol. "Seismic methods for fluid discrimination in areas with complex geologic history — A case example from the Barents Sea." Interpretation 8, no. 1 (February 1, 2020): SA35—SA47. http://dx.doi.org/10.1190/int-2019-0057.1.
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We have developed a new scheme for calculation of density ratio, an attribute that can be directly linked to hydrocarbon saturation, and applied it to seismic amplitude variation with offset (AVO) data from the Hoop area in the Barents Sea. The approach is based on the inversion of Zoeppritz’s equation for PP-wave. Furthermore, by using interval velocities, we quantified uplift magnitude for a given interval beneath Base Cretaceous unconformity (BCU) horizon in the Hoop area. Depending on the temperature gradient, the maximum burial depth can be estimated, a crucial factor affecting the elastic properties of the rocks. Coupling uplift map with temperature history for key stratigraphic units from basin modeling enabled us to extend the training data away from well control. By doing so, we created nonstationary AVO probability density functions (PDFs) for calibration and classification of seismic attributes in the test area. This decreases the likelihood of misclassification of pore fluid type as opposed to the case where the training data are created based only on sparse well-log data. We tested and compared the methods on the Barents Sea seismic data set, and the results were validated at four well locations. Finally, maps of fluid distribution obtained from stochastic rock-physics modeling honoring burial history were compared against the density ratio map. Four maps revealed the same anomalous zones, the major difference being the detection of the down-flank presence of oil associated with some of the predicted gas anomalies in the prospect area, in the case of density ratio map. Possible gas caps were detected/predicted only for certain temperature constraints during the AVO classifications and were most obvious in the density ratio map.
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Jesus, Carlos, Maria Olho Azul, Wagner Moreira Lupinacci, and Leandro Machado. "Multiattribute framework analysis for the identification of carbonate mounds in the Brazilian presalt zone." Interpretation 7, no. 2 (May 1, 2019): T467—T476. http://dx.doi.org/10.1190/int-2018-0004.1.
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Carbonate mounds, as described herein, often present seismic characteristics such as low amplitude and a high density of faults and fractures, which can easily be oversampled and blur other rock features in simple geobody extraction processes. We have developed a workflow for combining geometric attributes and hybrid spectral decomposition (HSD) to efficiently identify good-quality reservoirs in carbonate mounds within the complex environment of the Brazilian presalt zone. To better identify these reservoirs within the seismic volume of carbonate mounds, we divide our methodology into four stages: seismic data acquisition and processing overview, preconditioning of seismic data using structural-oriented filtering and imaging enhancement, calculation of seismic attributes, and classification of seismic facies. Although coherence and curvature attributes are often used to identify high-density fault and fracture zones, representing one of the most important features of carbonate mounds, HSD is necessary to discriminate low-amplitude carbonate mounds (good reservoir quality) from low-amplitude clay zones (nonreservoir). Finally, we use a multiattribute facies classification to generate a geologically significant outcome and to guide a final geobody extraction that is calibrated by well data and that can be used as a spatial indicator of the distribution of good reservoir quality for static modeling.
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Polom, U., I. Arsyad, and H. J. Kümpel. "Shallow shear-wave reflection seismics in the tsunami struck Krueng Aceh River Basin, Sumatra." Advances in Geosciences 14 (January 2, 2008): 135–40. http://dx.doi.org/10.5194/adgeo-14-135-2008.
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Abstract. As part of the project "Management of Georisk" (MANGEONAD) of the Federal Institute for Geosciences and Natural Resources (BGR), Hanover, high resolution shallow shear-wave reflection seismics was applied in the Indonesian province Nanggroe Aceh Darussalam, North Sumatra in cooperation with the Government of Indonesia, local counterparts, and the Leibniz Institute for Applied Geosciences, Hanover. The investigations were expected to support classification of earthquake site effects for the reconstruction of buildings and infrastructure as well as for groundwater exploration. The study focussed on the city of Banda Aceh and the surroundings of Aceh Besar. The shear-wave seismic surveys were done parallel to standard geoengineering investigations like cone penetrometer tests to support subsequent site specific statistical calibration. They were also partly supplemented by shallow p-wave seismics for the identification of (a) elastic subsurface parameters and (b) zones with abundance of groundwater. Evaluation of seismic site effects based on shallow reflection seismics has in fact been found to be a highly useful method in Aceh province. In particular, use of a vibratory seismic source was essential for successful application of shear-wave seismics in the city of Banda Aceh and in areas with compacted ground like on farm tracks in the surroundings, presenting mostly agricultural land use areas. We thus were able to explore the mechanical stiffness of the subsurface down to 100 m depth, occasionally even deeper, with remarkably high resolution. The results were transferred into geotechnical site classification in terms of the International Building Code (IBC, 2003). The seismic images give also insights into the history of the basin sedimentation processes of the Krueng Aceh River delta, which is relevant for the exploration of new areas for construction of safe foundations of buildings and for identification of fresh water aquifers in the tsunami flooded region.
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O. Oshogbunu, Fredrick, and Pius A. Enikanselu. "Machine learning approach to hydrocarbon zone prediction from seismic attributes over “GEM” field, Niger-Delta, Nigeria." International Journal of Advanced Geosciences 9, no. 2 (November 19, 2021): 88. http://dx.doi.org/10.14419/ijag.v9i2.31811.
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A computer programme (in Python language) was developed for the generation and performance assessment of predictive models, capable of combining relevant seismic attributes for reliable hydrocarbon zone prediction ahead of drilling. It attempts to resolve the problem of making accurate and efficient interpretations from a large database of derived seismic attributes. The research utilized post-stack 3D seismic volume for the delineation of structures and the generation of seismic attributes. Six faults were identified across the mapped horizons. Five seismic attributes were generated and exported from 3D seismic data as numerical values for machine learning analysis. The binary cross-entropy classification metric was used to evaluate the performance of the developed predictive models while an individual seismic attribute (Maximum Amplitude and Extract value) map was used to validate the predictive models. A correlation of well depth-to-top of selected horizons with the seismic depth slice surface was used for further model validation. The Multi-Layer Perceptron (MLP) model results enhanced the visibility of the other five hydrocarbon prediction zones. The MLP predictive model map gave higher precision of the predicted hydrocarbon zones over the Self-Organising Map (SOM) predictive model, thus reinforcing the confidence level of the former.
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Ialalov, D. M., M. V. Romashov, I. V. Oreshko, I. P. Vorontsov, and A. E. Ibraev. "Revising geological model for north flank of main production interval in oil field." Kazakhstan journal for oil & gas industry 2, no. 2 (June 15, 2020): 14–20. http://dx.doi.org/10.54859/kjogi95617.
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Based on the results of the previous works performed at the studied oil field, a significant potential for increasing initial oil reserves was established. Three wells were drilled in the edge zone of the field at the end of 2019, which confirmed the potential in the undrilled area of the northern flank of the filed. Until these wells were drilled, the edge zone was considered unpromising.The authors have been tasked to assess perspectives of development of edge zones and build a detailed geological model for that area. The detailed model is intended to forecast the evolution of elements of the fluvial system for based on drilling data and results of seismic researches in undrilled parts of field.In order to forecast the distribution zones of channel complexes, high-resolution 3D seismic data of 2019 were used. Based on the dynamic analysis, interpretation of the distribution zones of channel complexes and their classification within the studied reservoir layer were performed. The main focus was on identifying and mapping of small channels in the boundary zone of the reservoir with the aim of further detailed geological modeling and assessment of the development potential, taking into account new data.
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Koutsoupakis, Ioannis, Yiannis Tsompanakis, Pantelis Soupios, Panagiotis Kirmizakis, SanLinn Kaka, and Costas Providakis. "Seismic Risk Assessment of Chania, Greece, Using an Integrated Computational Approach." Applied Sciences 11, no. 23 (November 26, 2021): 11249. http://dx.doi.org/10.3390/app112311249.
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This study develops a comprehensive seismic risk model for the city of Chania, in Greece, which is located ina highly seismic-prone region due to the occurrenceof moderate to large earthquakes because of the nearby major subduction zone between African and Eurasian tectonic plates. The main aim is to reduce the seismic risk for the study area by incorporating the spatial distribution of the near-surface shear wave velocity model and the soil classification, along with all possible seismic sources, taking into account historical events. The study incorporates and correlates various ground motion scenarios and geological fault zones as well as information on existing buildings to develop a seismic risk model using QuakeIST software, and then the seismic hazard and a realistic prediction of resulting future adverse effects are assessed. The developed model can assist the municipal authorities of Chania to be prepared for potential seismic events, as well as city planners and decisionmakers, who can use the model as an effective decision-making tool to identify the seismic vulnerability of the city buildings and infrastructure. Thus, this study enables the implementation of an appropriate and viable earthquake-related hazards strategy to mitigate damage and losses in future earthquakes.
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Brescia, Manuela. "Rotation capacity and steel members classification criteria in seismic zones." Pollack Periodica 2, no. 2 (August 2007): 63–73. http://dx.doi.org/10.1556/pollack.2.2007.2.6.
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Decanini, Luis, Giacomo Di Pasquale, Paolo Galli, Fabrizio Mollaioli, and Tito Sanò. "Seismic Hazard and Seismic Zonation of the Region Affected by the 2002 Molise, Italy, Earthquake." Earthquake Spectra 20, no. 1_suppl (July 2004): 131–65. http://dx.doi.org/10.1193/1.1771012.
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In 1998, a new system of seismic classification promoted by the Department of Civil Protection identified the area in Italy hit by the 2002 earthquake in Molise and Puglia as a Zone 2 (moderately seismic). However, this classification was not adopted until March 2003, when an ordinance passed that partially closed the gap between scientific knowledge and official recognition of seismic hazard and that established a method for constantly updating the classification in the future. This paper reviews some of the methods available to assess the seismic hazard, particularly referring to the rich seismic history of Italy and using the “Associated Seismic Area” concept. This study confirms that the area affected by this earthquake should be considered as Zone 2. An appendix presents data on the seismic risk of existing buildings in the area and concludes that it is high for masonry buildings and that a strengthening program is needed.
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Del Gaudio, V., P. Pierri, and G. Calcagnile. "Seismogenic zonation and seismic hazard estimates in a Southern Italy area (Northern Apulia) characterised by moderate seismicity rates." Natural Hazards and Earth System Sciences 9, no. 1 (February 17, 2009): 161–74. http://dx.doi.org/10.5194/nhess-9-161-2009.
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Abstract. The northernmost part of Apulia, in Southern Italy, is an emerged portion of the Adriatic plate, which in past centuries was hit by at least three disastrous earthquakes and at present is occasionally affected by seismic events of moderate energy. In the latest seismic hazard assessment carried out in Italy at national scale, the adopted seismogenic zonation (named ZS9) has defined for this area a single zone including parts of different structural units (chain, foredeep, foreland). However significant seismic behaviour differences were revealed among them by our recent studies and, therefore, we re-evaluated local seismic hazard by adopting a zonation, named ZNA, modifying the ZS9 to separate areas of Northern Apulia belonging to different structural domains. To overcome the problem of the limited datasets of historical events available for small zones having a relatively low rate of earthquake recurrence, an approach was adopted that integrates historical and instrumental event data. The latter were declustered with a procedure specifically devised to process datasets of low to moderate magnitude shocks. Seismicity rates were then calculated following alternative procedural choices, according to a "logic tree" approach, to explore the influence of epistemic uncertainties on the final results and to evaluate, among these, the importance of the uncertainty in seismogenic zonation. The comparison between the results obtained using zonations ZNA and ZS9 confirms the well known "spreading effect" that the use of larger seismogenic zones has on hazard estimates. This effect can locally determine underestimates or overestimates by amounts that make necessary a careful reconsideration of seismic classification and building code application.
Book chapters on the topic "Seismic zones classifications"
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Zakeri, Negar Sadat Soleimani, and Saeid Pashazadeh. "Data Mining Techniques on Earthquake Data." In Improving Knowledge Discovery through the Integration of Data Mining Techniques, 183–99. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8513-0.ch010.
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Active faults are sources of earthquakes and one of them is north fault of Tabriz in the northwest of Iran. The activation of faults can harm humans' life and constructions. The analysis of the seismic data in active regions can be helpful in dealing with earthquake hazards and devising prevention strategies. In this chapter, structure of earthquake events along with application of various intelligent data mining algorithms for earthquake prediction are studied. Main focus is on categorizing the seismic data of local regions according to the events' location using clustering algorithms for classification and then using intelligent artificial neural network for cluster prediction. As a result, the target data were clustered to six groups and proposed model with 10 fold cross validation yielded accuracy of 98.3%. Also, as a case study, the tectonic stress on concentration zones of Tabriz fault has been identified and five features of the events were used. Finally, the most important points have been proposed for evaluation of the nonlinear model predictions as future directions.
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Moores, Eldridge M., Nathan Simmons, Asish R. Basu, and Robert T. Gregory. "The Indian Ocean, its supra-subduction history, and implications for ophiolites." In Plate Tectonics, Ophiolites, and Societal Significance of Geology: A Celebration of the Career of Eldridge Moores. Geological Society of America, 2021. http://dx.doi.org/10.1130/2021.2552(01).
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ABSTRACT Ophiolite complexes represent fragments of ocean crust and mantle formed at spreading centers and emplaced on land. The setting of their origin, whether at midocean ridges, back-arc basins, or forearc basins has been debated. Geochemical classification of many ophiolite extrusive rocks reflect an approach interpreting their tectonic environment as the same as rocks with similar compositions formed in various modern oceanic settings. This approach has pointed to the formation of many ophiolitic extrusive rocks in a supra-subduction zone (SSZ) environment. Paradoxically, structural and stratigraphic evidence suggests that many apparent SSZ-produced ophiolite complexes are more consistent with mid-ocean ridge settings. Compositions of lavas in the southeastern Indian Ocean resemble those of modern SSZ environments and SSZ ophiolites, although Indian Ocean lavas clearly formed in a mid-ocean ridge setting. These facts suggest that an interpretation of the tectonic environment of ophiolite formation based solely on their geochemistry may be unwarranted. New seismic images revealing extensive Mesozoic subduction zones beneath the southern Indian Ocean provide one mechanism to explain this apparent paradox. Cenozoic mid-ocean-ridge–derived ocean floor throughout the southern Indian Ocean apparently formed above former sites of subduction. Compositional remnants of previously subducted mantle in the upper mantle were involved in generation of mid-ocean ridge lavas. The concept of historical contingency may help resolve the ambiguity on understanding the environment of origin of ophiolites. Many ophiolites with “SSZ” compositions may have formed in a mid-ocean ridge setting such as the southeastern Indian Ocean.
Conference papers on the topic "Seismic zones classifications"
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Kumar, Rajive, T. Al-Mutairi, P. Bansal, Khushboo Havelia, Faical Ben Amor, Bassam Farhan, Aya Ibrahim, et al. "Connecting the Dots between Geology and Seismic to Mitigate Drilling Risks: Mapping & Characterization of the High Pressure High Temperature Gotnia Formation in Kuwait." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207452-ms.
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Abstract As Kuwait focuses on developing the deep Jurassic reservoirs, the Gotnia Formation presents significant drilling challenges. It is the regional seal, consisting of alternating Salt and Anhydrite cycles, with over-pressured carbonate streaks, which are also targets for future exploration. The objective of this study was to unravel the Gotnia architecture, through detailed mapping of the intermediate cycles, mitigating drilling risks and characterizing the carbonate reservoirs. A combination of noise attenuation, bandwidth extension and seismic adaptive wavelet processing (SAWP)) was applied on the seismic data, to improve the signal-to-noise ratio of the seismic data between 50Hz to 70Hz and therefore reveal the Anhydrite cycles, which house the carbonate streaks. The Salt-Anhydrite cycles were correlated, using Triple Combo and Elastic logs, in seventy-six wells, and spatially interpreted on the band-limited P-impedance volume, generated through pre-stack inversion. Pinched out cycles were identified by integrating mud logs with seismic data and depositional trends. Pre-stack stochastic inversion was performed to map the thin carbonate streaks and characterize the carbonate reservoirs. The improved seismic resolution resulted in superior results compared to the legacy cube and aided in enhancing the reflector continuity of Salt-Anhydrite cycles. In corroboration with the well data, three cycles of alternating salt and anhydrite, with varying thickness, were mapped. These cycles showed a distinctive impedance contrast and were noticeably more visible on the P-impedance volume, compared to the seismic amplitude volume. The second Anhydrite cycle was missing in some wells and the lateral extension of the pinch-outs was interpreted and validated based on the P-impedance volume. As the carbonate streaks were beyond the seismic resolution, they were not visible on the Deterministic P-impedance. The amount of thin carbonate streaks within the Anhydrite cycles could be qualitatively assessed based on the impedance values of the entire zone. Areas, within the zone, with a higher number of and more porous carbonate streaks displayed lowering of the overall impedance values in the Anhydrite zones, and could pose drilling risks. This information was used to guide the pre-stack stochastic inversion to populate the thin carbonate streaks and generate a high-resolution facies volume, through Bayesian Classification. Through this study, the expected cycles and over-pressured carbonate layers in the Gotnia formation were predicted, which can be used to plan and manage the drilling risks and reduce operational costs. This study presents an integrated and iterative approach to interpretation, where the well log analysis, seismic inversion and horizon interpretation were done in parallel, to develop a better understanding of the sub-surface. This workflow will be especially useful for interpretation of over-pressured overburden zones or cap rocks, where the available log data can be limited.
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Gusev, Sergey Igorevich, Elena Sergeyevna Kolbikova, Olga Igorevna Malinovskaya, Azat Fanisovich Garaev, and Robert Kamilevich Valiev. "Forecast of Prospective Oil Saturation Zones in the Devonian Carbonate Deposits of the Kharyaginsky Field Based on Geological and Geophysical Information Analysis by Using Machine Learning Methods." In SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206520-ms.
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Abstract The Kharyaginskoye oil field is located on the territory of the Nenets Autonomous District and belongs to the Timan-Pechora Basin oil and gas province. The main object of development is a Devonian age carbonate reservoir. The productive zones of the studied object are mainly confined to thin bed low-porosity reservoirs with a complex structure of void space. The high heterogeneity of deposits laterally and the presence of different levels of oil-water contact (OWC) in the marginal isolated zones necessitate a more accurate assessment of the oil-saturated effective thicknesses. The increase in the reliability of the interpretation was achieved by the joint analysis of borehole and seismic studies using Machine Learning methods. At the stage of configuring the facies model based on well logs and core data, a Multi-Resolution Graph-based Clustering MRGC was used, which provides effective integration of geological and geophysical information. The multi-dimensional dot-pattern recognition method based on k-Nearest neighbors algorithm (k-NN), and by combining various criteria, it allows solving the problem of non-linearity of the relationships between logging responses and the corresponding lithology. The algorithm of the democratic association of neural networks DNNA was used to propagate electrofacies in the inter-well space. The method optimizes the use of seismic data before summation and after summation together with well data through a controlled process that provides a calibrated and scaled distribution of facies. The most probable facies distribution can be used directly as a property in reservoir modeling or as a constraint for modeling. It is known that there is no direct connection between a certain type of wave pattern and the lithological composition of rocks, therefore, the analysis of changing reflection characteristics is performed in conjunction with geophysical data, such as well logging. In addition, a priori geological information about the work area is involved. An important condition for the effective application of facies analysis is the presence of representative core material and the availability of high-quality well information. At the first stage of the work, the lithotyping of carbonate deposits was performed according to the macro description of the core, based on the classification of limestones according to R. H. Dunham. Then, using the multidimensional statistical recognition algorithm MRGC, the relationships between the selected lithotypes and logging responses were obtained. As a result of the tuning, a cluster model was obtained that allows us to distinguish electrofacies characterized by an increased filtration and capacitance potential. At the second stage, the obtained electrofacies, considering the nature of saturation, were used to train cubes of seismic attributes and calculate the cubes of lithofacies and the probability of the existence of each lithofacies. The key point in the distribution was the use of electrofacies obtained in wells belonging to different facies zones. Thus, the joint analysis of all available borehole and seismic information by machine learning methods made it possible to make a forecast lithofacies considering the type of saturation based on geological and geophysical information analysis. The effectiveness of the presented technologies was demonstrated by analyzing the properties of low-permeable carbonate reservoirs, where classical attributes and inversion demonstrate limitations in describing a heterogeneous saturation model. The use of neural network approaches allows to configure complex nonlinear dependencies that are not available to classical methods. The use of a small volume of multi-scale geological and geophysical information using Machine Learning algorithms in the field of field-geophysical and seismic interpretation makes it possible to increase the reliability of interpretation and clarify the location of prospective zones with improved reservoir properties on the studied area, as well as to minimize geological risks during subsequent well placement.
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Erfani, F., H. Hashemi, and M. Sadiq-Arabani. "Application of Local Fisher Discrimnant Analysis on a seismic classification experiment: Case Study, a gas hydrate zone in Oman Sea." In Istanbul 2012 - International Geophysical Conference and Oil & Gas Exhibition. Society of Exploration Geophysicists and The Chamber of Geophysical Engineers of Turkey, 2012. http://dx.doi.org/10.1190/ist092012-001.12.
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Ciabarri, Fabio, Marco Pirrone, and Cristiano Tarchiani. "ANALYTICAL UNCERTAINTY PROPAGATION IN FACIES CLASSIFICATION WITH UNCERTAIN LOG-DATA." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0071.
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Log-facies classification aims to predict a vertical profile of facies at well location with log readings or rock properties calculated in the formation evaluation and/or rock-physics modeling analysis as input. Various classification approaches are described in the literature and new ones continue to appear based on emerging Machine Learning techniques. However, most of the available classification methods assume that the inputs are accurate and their inherent uncertainty, related to measurement errors and interpretation steps, is usually neglected. Accounting for facies uncertainty is not a mere exercise in style, rather it is fundamental for the purpose of understanding the reliability of the classification results, and it also represents a critical information for 3D reservoir modeling and/or seismic characterization processes. This is particularly true in wells characterized by high vertical heterogeneity of rock properties or thinly bedded stratigraphy. Among classification methods, probabilistic classifiers, which relies on the principle of Bayes decision theory, offer an intuitive way to model and propagate measurements/rock properties uncertainty into the classification process. In this work, the Bayesian classifier is enhanced such that the most likely classification of facies is expressed by maximizing the integral product between three probability functions. The latters describe: (1) the a-priori information on facies proportion (2) the likelihood of a set of measurements/rock properties to belong to a certain facies-class and (3) the uncertainty of the inputs to the classifier (log data or rock properties derived from them). Reliability of the classification outcome is therefore improved by accounting for both the global uncertainty, related to facies classes overlap in the classification model, and the depth-dependent uncertainty related to log data. As derived in this work, the most interesting feature of the proposed formulation, although generally valid for any type of probability functions, is that it can be analytically solved by representing the input distributions as a Gaussian mixture model and their related uncertainty as an additive white Gaussian noise. This gives a robust, straightforward and fast approach that can be effortlessly integrated in existing classification workflows. The proposed classifier is tested in various well-log characterization studies on clastic depositional environments where Monte-Carlo realizations of rock properties curves, output of a statistical formation evaluation analysis, are used to infer rock properties distributions. Uncertainty on rock properties, modeled as an additive white Gaussian noise, are then statistically estimated (independently at each depth along the well profile) from the ensemble of Monte-Carlo realizations. At the same time, a classifier, based on a Gaussian mixture model, is parametrically inferred from the pointwise mean of the Monte Carlo realizations given an a-priori reference profile of facies. Classification results, given by the a-posteriori facies proportion and the maximum a-posteriori prediction profiles, are finally computed. The classification outcomes clearly highlight that neglecting uncertainty leads to an erroneous final interpretation, especially at the transition zone between different facies. As mentioned, this become particularly remarkable in complex environments and highly heterogeneous scenarios.
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Al Naqbi, S., J. Ahmed, J. Vargas Rios, Y. Utami, A. Elila, A. Salahuddin, Khushboo Havelia, et al. "Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207583-ms.
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Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.
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Seitkhaziyev, Yessimkhan Sherekhanovich, Rakhim Nagangaliyevich Uteyev, and Nariman Danebekovich Sarsenbekov. "Application of Biomarkers and Oil Fingerprinting for Genetic Classification of Oil and Prediction of Petroleum Migration Pathways of Aryskum Downfold of South-Torgay Depression." In SPE Annual Caspian Technical Conference. SPE, 2021. http://dx.doi.org/10.2118/207037-ms.
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Abstract This article presents the results of fingerprinting and biomarker analysis of 254 oil samples derived from 11 different fields and structures in Aryskum downfold of the South-Torgay depression for genetic classification of oils and prediction of petroleum migration pathways. According to the oil fingerprinting results based on patented Shell technology, 12 groups of oils were found: oils in reservoirs of central part of Nuraly field form the first group, while oils in the producing horizons of Western Nuraly, Southern Khayrgeldy, Akshabulak East and fluvial beds of Central Akshabulak fields form the second group. The oils related to the third group were found in the wells exploiting producing horizon I in the north dome of Central Akshabulak, while the oils from wells penetrating lower producing horizons(III-IV-V) of Central Akshabulak, upper producing horizons in South dome of Central Akshabulak and of all producing horizons of the north dome of Akshabulak South constitute the fourth group. The fifth group includes only one oil sample of different genesis from well № 37, which penetrates the paleo-channel №13 at South Akshabulak. The genetic difference of this oil from other oils was also confirmed by its biomarker composition. Most of oil fingerprinting star plots in Aksay field are identical and form the sixth group, although the seventh group comprises only one oil № 47 in Aksay. Sample set with №8 was discovered in the pay zones of Taur field and well № 75, exploiting the same horizon in the northern part of Aksay. The ninth oil group was identified in cretaceous producing layers of the Khairgeldy South-West field and Jurassic beds of the Khargeldy North field, while the identical composition of the cretaceous oil from the Khairgeldy North and Khairgeldy fields forms the tenth group. The last eleventh group includes oil from well №. 12 on South-west Khairgeldy, although it has some similarities with Taur oils. For 20 oil samples was carried out biomarker analysis, according to the results of which all studied oils were formed in terrigenous (shaly) OM, deposited in lacustrine environment. Oils from central Nuraly are more thermally mature and lighter in density than those from western Nuraly. Oils of Akshabulak East are thermally less mature than oils of Central Akshabulak and Akshabulak South despite its deeper deposition. Based on the performed analysis, in the conclusion were presented 5 prospective hydrocarbon accumulation zones for increasing hydrocarbon reserves in the future. But the results of the performed studies provide valuable information only when integrated with confirmed geological and seismic data.
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Seitkhaziyev, Yessimkhan Sherekhanovich, Rakhim Nagangaliyevich Uteyev, and Nariman Danebekovich Sarsenbekov. "Application of Biomarkers and Oil Fingerprinting for Genetic Classification of Oil and Prediction of Petroleum Migration Pathways of Aryskum Downfold of South-Torgay Depression." In SPE Annual Caspian Technical Conference. SPE, 2021. http://dx.doi.org/10.2118/207037-ms.
Full textAbstract:
Abstract This article presents the results of fingerprinting and biomarker analysis of 254 oil samples derived from 11 different fields and structures in Aryskum downfold of the South-Torgay depression for genetic classification of oils and prediction of petroleum migration pathways. According to the oil fingerprinting results based on patented Shell technology, 12 groups of oils were found: oils in reservoirs of central part of Nuraly field form the first group, while oils in the producing horizons of Western Nuraly, Southern Khayrgeldy, Akshabulak East and fluvial beds of Central Akshabulak fields form the second group. The oils related to the third group were found in the wells exploiting producing horizon I in the north dome of Central Akshabulak, while the oils from wells penetrating lower producing horizons(III-IV-V) of Central Akshabulak, upper producing horizons in South dome of Central Akshabulak and of all producing horizons of the north dome of Akshabulak South constitute the fourth group. The fifth group includes only one oil sample of different genesis from well № 37, which penetrates the paleo-channel №13 at South Akshabulak. The genetic difference of this oil from other oils was also confirmed by its biomarker composition. Most of oil fingerprinting star plots in Aksay field are identical and form the sixth group, although the seventh group comprises only one oil № 47 in Aksay. Sample set with №8 was discovered in the pay zones of Taur field and well № 75, exploiting the same horizon in the northern part of Aksay. The ninth oil group was identified in cretaceous producing layers of the Khairgeldy South-West field and Jurassic beds of the Khargeldy North field, while the identical composition of the cretaceous oil from the Khairgeldy North and Khairgeldy fields forms the tenth group. The last eleventh group includes oil from well №. 12 on South-west Khairgeldy, although it has some similarities with Taur oils. For 20 oil samples was carried out biomarker analysis, according to the results of which all studied oils were formed in terrigenous (shaly) OM, deposited in lacustrine environment. Oils from central Nuraly are more thermally mature and lighter in density than those from western Nuraly. Oils of Akshabulak East are thermally less mature than oils of Central Akshabulak and Akshabulak South despite its deeper deposition. Based on the performed analysis, in the conclusion were presented 5 prospective hydrocarbon accumulation zones for increasing hydrocarbon reserves in the future. But the results of the performed studies provide valuable information only when integrated with confirmed geological and seismic data.
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Salim, Sadek, Mayada Sayed, Ibrahim Abdo, Emad Abdel Hakim, Mohamed Farouk, Amr Hegazy, Abdelmoniem El Araby, et al. "Developing a Fit-For-Basin Novel Solution with the First Application of iCore Behind Cased Borehole in a Complex Heterogeneous Miocene Carbonate Reservoir: Bakr Oil Field, Central Province of Gulf of Suez." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210230-ms.
Full textAbstract:
Abstract Gulf of Suez basin is one of the most complex areas of exploration that requires a fit-for-basin solution to reveal the true potential of the carbonate reservoirs. The multi-domain integration of the interpreted lithology from high-resolution imaging tools recorded in the open hole section with electrofacies integrated with NMR and cased hole elemental spectroscopy data provides the 1st time application to derive synthetic core with high-resolution facies in the drilled wells with complex heterogeneity challenges. 3D seismic attributes, stratigraphical and structural analysis, revealed a potential three-way dip closure with an expected high-quality carbonate reservoir. An automated processing workflow converts gamma-ray yields from the energy spectrum measured behind casing into the dry weight and mineral fractions. The computed mineralogical outputs are then described based on a standardized ternary diagram approach to generate dry-weight mineralogy-based lithofacies. The synthetic high-resolution lithofacies are integrated with MDT, NMR, and spectroscopy to capture mobility, type of fluid, and saturation associated with lithofacies changes which is integrated with well integrity analysis to plan, design, and execute of innovative technique for carbonate stimulation. This paper demonstrates reviving exploration activity in one of the brownfields and the first application for borehole imaging integrated with cased hole spectroscopy on the recent discovery well to select perforation. Once a robust lithofacies classification is obtained, this is used for detailed stratigraphic analysis, well to well correlation, cross-sections, mapping or refined static reservoir modeling, and perforation zones selection which represents the first success story for this innovative technique. This multi-domain integration helped to design a customized acidizing technique to reveal the true reservoir potential that had a 3 fold increase in productivity index compared to offset fields in the same basin. The workflow can be applied to multiple cases as a cost-effective solution in multiple scenarios and different formation types especially if there is no core available in old wells. Furthermore, the innovative acidizing technique can effectively stimulate any carbonate reservoir.
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Suwannasri, Krongrath, Cheong Yaw Peng, Sirada Asawachaisujja, Ratchadaporn Uttareun, Orapan Limpornpipat, Apichart Suphawajruksakul, and Pongsit Chongrueanglap. "Encapsulating Complex Carbonate Facie Heterogeneity into Static Reservoir Model through Seismic-Based Characterization, Lang-Lebah Field, Central Luconia, Offshore Sarawak." In Offshore Technology Conference Asia. OTC, 2022. http://dx.doi.org/10.4043/31517-ms.
Full textAbstract:
Abstract Capturing the reservoir heterogeneity is crucial for optimizing field development. Lang-Lebah field is a Miocene carbonate platform with approximately 5 sq.km. in size and over 1 km in height with a high degree of heterogeneity in both vertical and horizontal directions. In this study, we conducted a seismic-based characterization to capture reservoir heterogeneity and then ran sequential gaussian simulation with a data from wells to build a static model for field development purpose. The method mainly comprises of four steps. The first step is to establish a relationship between reservoir properties (such as facie and porosity) to elastic properties (such as P- and S-wave impedances) to build conditional probability. The second step is running pre-stack inversion to derive P- and S-wave impedances as inputs for the third step. The posterior probability of each facie is determined through Bayesian classification using inverted impedances and the derived conditional probability as inputs. The last step is employing sequential gaussian simulation to build a static model using derived posterior probability of each facie and porosity cube. The static model encapsulates heterogeneity in terms of carbonate facie and reservoir properties. The observed heterogeneity is highly consistent with the understanding of geological model of this carbonate platform. The result shows lateral heterogeneity in each zone of high energy facies (such as reef margin) at the windward flank of the platform and low energy facies (such as lake) at platform interior. Thus, this result was elaborated for geological concept beyond the using well data alone. The result also shows a vertical succession from different carbonate reservoir deposit regarding to accommodation as carbonate build-out to a typical carbonate platform build-up continue to carbonate build-in. In addition, flooding event or surfaces, which is part of reservoir barrier, was also identified and included in this static model. The details of this successful novel study lay a fundamental work process for battling the challenge of gigantic carbonate characterization for field development. Because of this sophisticated model, we can properly plan the sequence of production and producing well targeting based on the derived reservoir heterogeneity resulting in enabling several Tscf of reserves and minimizing development costs.