Bullentin of Canadian Petroleum Geology最新文献

{"title":"New insights into the Bedrock and Quaternary Geology of St. George’s Bay from a vertical integration of marine datasets, offshore western Newfoundland","authors":"L. Dafoe, J. Shaw, C. Jauer, P. Giles, J. Waldron, D. Potter","doi":"10.2113/GSCPGBULL.64.1.1","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.64.1.1","url":null,"abstract":"St. George’s Bay lies south of Port au Port Peninsula on the west coast of Newfoundland, Canada. The Carboniferous Bay St. George subbasin extends beneath the bay and onshore to the southeast. The subbasin is an extension of the Magdalen Basin, a Carboniferous basin with probable resource potential, part of the broader Maritimes Basin. The onshore geology of the subbasin is relatively well understood. Marine datasets from the less well known offshore portion include high-resolution bathymetry, aeromagnetic data, shallow and industry wells, and both shallow and deeper (conventional) two-dimensional seismic data. Quaternary features were previously documented in inner St. George’s Bay where the surficial cover is thick. In the outer part of the bay, bedrock ridges are draped by iceberg-turbated glaciomarine mud and moraines. Beneath this cover, reinterpreted palynological analyses in the A-36 well reflect only Visean Codroy Group. The uppermost Codroy Group of this well may correlate with nearby red sandstones from shallow marine bedrock drill cores; however, younger Carboniferous rocks may also be present. Reflectors mapped using conventional seismic data mark the base Codroy Group and the top of a salt unit in the Codroy Group. Salt-cored anticlines and synclines are associated with halokinesis, and with a fault system with both dip-slip and dextral-slip components. Multibeam bathymetry and the second vertical derivative of the magnetic field delineate the folds as curvilinear, en echelon, and, in some cases, doubly plunging. Beneath the subbasin, seismic and aeromagnetic data indicate Proterozoic basement blocks and an offshore extension of the early Paleozoic Romaines Brook Fault. The Carboniferous structures suggest an environment of dextral transtension that reactivated early Paleozoic structures aligned with pre-existing weaknesses in basement.","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.64.1.1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68208026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic types and accumulation models for biogenic gases in Bohai Bay Basin, eastern China","authors":"Jin Qiang, Cheng Fuqi, Gao Yang, Chen Liang","doi":"10.2113/GSCPGBULL.64.1.47","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.64.1.47","url":null,"abstract":"Abstract Bohai Bay basin, in eastern China, is rich in oil resources, but recently large natural gas accumulations have been found, mainly in Miocene reservoirs, at depths shallower than 1500 m. Molecular and stable carbon isotopic gas compositions show that the gases have several distinctive biogenic origins. The first type of biogenic gas is generated in anaerobic environments from thermally immature organic matter and this type of gas accumulates typically in reservoirs interbedded within the immature source rock succession. The second type occurs up dip or above heavy-oil reservoirs and the gas is inferred derived from crude oils in shallow reservoirs as a biodegradation product. The third type originates from source rocks that were buried deeply previously (>2800 m in the basin), where they were “geopasteurized”, but which are now uplifted and recolonized by microbes at their current shallow depths. The first type is generally referred to as primary biogenic gas. The second type is commonly referred to as secondary biogenic gases elsewhere, but herein is called biogenic gas from biodegraded oil (BG-BO). This distinguishes them from the third type which is described herein as secondary biogenic gas and which is distinguished from BG-BO. Bohai Bay Basin gas pools often contain one of the three biogenic gas types and are often mixed, more or less, with thermogenic natural gas from petroleum pools in Eocene reservoirs. It is therefore important to distinguish the genetic affinities of the biogenic natural gases as an aid to the development and exploration for additional biogenic gas resources in Bohai Bay Basin.","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.64.1.47","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68208357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reference Section for the Horn River Group and Definition of the Bell Creek Member, Hare Indian Formation in central Northwest Territories","authors":"L. Pyle, L. Gal","doi":"10.2113/GSCPGBULL.64.1.67","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.64.1.67","url":null,"abstract":"Abstract A stratigraphically complete section, exposed along a tributary to the Mountain River in the Northwest Territories, provides a reference section for the Devonian Horn River Group (Hare Indian, Ramparts, and Canol formations). Detailed whole rock geochemical data and total organic carbon content variations are related to the lithostratigraphy, thus providing a standard for regional correlation. The name ‘Bell Creek Member’ is proposed for the upper part of the Hare Indian Formation known as the “grey shale member”, and is defined at the Mountain River section. This member is heterogeneous and consists of two lithofacies: 1) a green-grey, silty shale and limestone facies (Bell Creek Member, grey facies); and 2) a dark grey, silty shale and limestone facies (Bell Creek Member, dark facies). The dark grey facies is described from a reference section along the Carcajou River, and is distributed south of 65°N latitude. The Bell Creek Member is Givetian in age.","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.64.1.67","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68208592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic Regridding of Geological Models for Flow Simulation","authors":"S. Lajevardi, C. Deutsch, O. Babak","doi":"10.2113/GSCPGBULL.63.4.374","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.63.4.374","url":null,"abstract":"Abstract Regridding geological models to a higher resolution for flow simulation is an important problem in geostatistical modeling. For practical reasons, over a large area, models can only be built at a relatively coarse resolution. Subsequently, the resolution of specified regions of interest must be increased before upscaling for flow modeling. The construction of a high-resolution model of the entire reservoir at the beginning of the evaluation may be impractical because of computational and time constraints. It is standard practice to implement nearest neighbor interpolation to increase the resolution of models. Although it is a simple practical solution, nearest neighbor interpolation introduces spatial continuity artifacts that are often unrealistic. This paper proposes an automatic stochastic regridding approach based on simulation. The simulation is conditioned to the initial coarse resolution model/realization. The process includes the extraction of specified regions of interest, definition of corresponding local variography, and implementation of Sequential Gaussian Simulation (SGS) and/or Sequential Indicator Simulation (SIS) to characterize continuous and categorical variables, respectively. In each specified region, the local variography can be defined by either implementing automatic fitting algorithms or assigning the global variography initially used to build the coarse resolution model. The regridding process is automated. The advantage of this approach over the conventional nearest neighbor interpolation is in the improvement in the realistic spatial variability features of small scale geologic heterogeneity. The benefits of obtaining a proper regridded model are discussed in a case study of a fluvial reservoir in the McMurray formation. One of the main reasons for generating high resolution models is in the appropriate characterization of small scale impermeable geobodies such as remnant shales. The coarse resolution models are not able to properly characterize the small scale geologic features of the shales; more amount of information is required to characterize smaller scale features. The metric of performance considered is the effective vertical permeability. The automated stochastic regridding workflow described in this paper is available on a Fortran platform with additional scripting which will be distributed upon request. Note that the terms “regridding” and “stochastic regridding” are used interchangeably and both refer to the proposed workflow of modeling at higher resolution.","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.63.4.374","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68207835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in Micromodeling using Resistivity Borehole Images","authors":"J. Manchuk, D. Garner, C. Deutsch","doi":"10.2113/GSCPGBULL.63.4.333","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.63.4.333","url":null,"abstract":"Abstract Resistivity borehole images offer a high resolution source of data that is valuable for reservoir characterization. Currently, available imaging tools offer high quality measurements with low susceptibility to borehole conditions or artifacts. There is a high correlation between the image measurements and larger scale resistivity logs such as deep induction or laterologs. In formations such as the McMurray Formation in Alberta, there is also a strong correlation between resistivity and volume of shale. Borehole images are used to obtain high resolution estimates of fluid saturation, porosity and volume of shale. Coarser scale petrophysical logs provide information to solve the inversion problem along with a shaly-sand conductivity approximation such as the Simandoux equation or Waxman-Smits model. Resulting high resolution properties can be used to construct micromodels of the reservoir for extracting effective permeability at arbitrary scales.","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.63.4.333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68208053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the Special Edition from the 2014 Gussow Conference on Advances in Applied Geomodeling","authors":"D. Garner, O. Babak, C. Deutsch","doi":"10.2113/GSCPGBULL.63.4.275","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.63.4.275","url":null,"abstract":"Geomodeling has proliferated among earth science and engineering professionals as a body of techniques, software packages, and workflows for subsurface reservoir characterization. Although not a recognized professional discipline or university degree option, geomodeling is a multidisciplinary subject with a growing technical community. Geomodeling is treated as an enabling technical field and focal point in the petroleum industry subsurface teams, with major software development dedicated to the subject and practitioners assuming the role and title. The broad subject typically draws from the fields of geology, geophysics, geostatistics, petrophysics, reservoir engineering, and increasingly geomechanics, computer science, and data analytics. The field of geostatistics is a fundamental aspect of geomodeling, providing many core algorithms. The other associated fields provide concepts, context, inputs, constraints, and direction for the technology applications and for multidisciplinary team efforts to deliver meaningful models and results.\u0000\u0000The motivation for companies is to pursue exploration, development, and production with increased efficiency and sustainability. The geomodeling proposition is to add value through improved reservoir management decisions. More accurate and precise geomodels lead to improved well planning and prediction of the behavior of alternative extraction technologies. Thus, geomodeling will improve recovery and reduce risk. Yet, gaps exist between the application of geomodeling, geostatistical methods, capabilities of software tools, and appropriate practice and ease of use. With …","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.63.4.275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68207322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stratigraphic Rule-based Reservoir Modeling","authors":"M. Pyrcz, R. Sech, J. Covault, B. Willis, Z. Sylvester, T. Sun","doi":"10.2113/GSCPGBULL.63.4.287","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.63.4.287","url":null,"abstract":"Abstract Stratigraphic rule-based modeling methods approximate sedimentary dynamics to generate numerical descriptions of reservoir architecture and the spatial distribution of petrophysical properties. A few intuitive rules included in a reservoir model construction workflow are shown to render realistic reservoir heterogeneity, continuity, and spatial organization to petrophysical property distributions that are difficult to obtain using conventional reservoir modeling methods. These rules may be inferred from mature reservoirs, surface and subsurface datasets, and process-based models. Examples include confinement, meander, compensation, and healing rules. By incorporating stratigraphic rules that relate to the underlying geological processes in temporal sequence, rule-based modeling methods offer more realistic representation of inferred reservoir heterogeneity beyond conventional geostatistical reservoir modeling approaches. These include variogram-based, multiple point-based and object-based approaches that rely on a limited set of spatial statistics to describe the products of geological processes. Moreover, since these methods operate within a geostatistical framework, uncertainty can be explored by varying geologically meaningful parameters over multiple scenarios and realizations whilst maintaining consistency with input data constraints and applied to reservoir modeling studies within standard workflows. Rule-based modeling methods enable a variety of applications, including use: directly as reservoir models, as a source of reservoir model input statistics such as variograms and training images, and as a numerical analog laboratory to explore relationships between data, model choices and forecasts. Challenges remain, such as reliability of emergent features, alignment to grid framework, and feasibility for broad application. Despite challenges, rule-based methods can offer uplift when the natural facies continuity patterns and their corresponding petrophysical properties are critical to support decisions in reservoir modeling projects.","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.63.4.287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68207754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Three Geostatistical Techniques for Realistic Geological Reservoir Modeling Integrating Multi-scale Data","authors":"B. Doligez, M. Ravalec, S. Bouquet, M. Adelinet","doi":"10.2113/GSCPGBULL.63.4.277","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.63.4.277","url":null,"abstract":"Abstract This paper discusses new methodologies and workflows developed to generate geological models (1) that look more realistic geologically speaking and (2) that respect the well and seismic data characterizing the studied area. Accounting simultaneously for these two constraints is challenging as they behave the opposite way. The more realistic the geological model, the more difficult the integration of data. A first powerful approach is based upon the non-stationary plurigaussian simulation method. In this case, the available geological and seismic data make it possible to compute the 3D probability distributions of facies proportions, which are then used to truncate the Gaussian functions. A second method is rooted in the Bayesian sequential simulation. Recent developments have been proposed to extend this method to media including distinct facies. We suggest an improved variant to better account for the resolution differences between sonic logs and seismic data. This yields a more robust framework to integrate seismic data. A third innovative approach reconciles geostatistical multipoint simulation with texture synthesis principles. Geostatistical multipoint methods provide models, which better reproduce complex geological features. However, they still call for significant computation times. On the other hand, texture synthesis has been developed for computer graphics: it can help reduce computation time, but it does not account for data. We then envision a hybrid multi-scale algorithm with improved computation performances and yet able to respect data","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.63.4.277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68208131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Cross-discipline Collaboration and Forward Modeling through Advanced Subsurface Geocellular Earth Modeling","authors":"R. Dusterhoft, S. Siddiqui, C. Davila","doi":"10.2113/GSCPGBULL.63.4.393","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.63.4.393","url":null,"abstract":"Abstract In many situations, our industry today has become very focused on managing huge quantities of data, looking for simple correlations that would enable them to capture useful information applicable to asset planning and field development. One of the key issues with this approach is that it is always looking backward to determine the path forward. By nature, this process is reactionary, making it difficult to identify opportunities for real innovation. Having assessed the industry position, a new approach was examined where data and information could be continuously fed into an evolving sub-surface geocellular earth modeling tool. This would represent a significant change from the traditional modeling tools where changes are limited due to the time and effort required to collect and process new information, then work through the entire modeling process. This new approach requires a geocellular earth model that is capable of receiving new information continuously and updating quickly. Forward modeling in this way provides a single environment where geoscientists and engineers can work together to improve their understanding of the reservoir, leverage the latest generation of tools to model cause and effect behaviors, and quickly establish optimized field development solutions. Statistical tools are still very useful for monitoring performance, but this data is also used to calibrate design tools to enable continuous refinement to the subsurface geocellular model and forward modeling tools. By accomplishing this, a common collaboration environment is created where both geoscientists and engineers can collaborate and work with the most current and most relevant subsurface information and knowledge. This concept has been tested in a number of proof-of-concept projects that have shown very promising results, one of which is discussed in detail in this paper.","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.63.4.393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68207900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface Prediction using Rejection Sampling to Handle Non-linear Constraints","authors":"P. Abrahamsen, P. Dahle, V. Hauge, Ariel Almendral-Vazquez, Maria Vigsnes","doi":"10.2113/GSCPGBULL.63.4.304","DOIUrl":"https://doi.org/10.2113/GSCPGBULL.63.4.304","url":null,"abstract":"Abstract We demonstrate accurate prediction of geological surfaces by imposing consistent physical and stochastic relationships between surfaces. The accuracy is improved by using all relevant information collected in wells: well points, zonation in horizontal sections, and gas/fluid content along wells. The conditioned surfaces are used to provide estimates of gross rock volumes of oil and gas reservoirs. In particular, it is shown how knowledge of spill point and zonation along well paths affect trapped volumes. A plain rejection sampling technique is used to deal with the highly non-linear relationships between a surface and its spill point. For well path conditioning, an extension of kriging to treat inequality constraints is proposed. It is based on efficient rejection sampling from a high dimensional truncated multivariate Gaussian distribution. The impact on gross rock volume distributions from different assumptions and data types is demonstrated by examples and the uncertainties in all the involved data types are consistently handled and quantified.","PeriodicalId":56325,"journal":{"name":"Bullentin of Canadian Petroleum Geology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2113/GSCPGBULL.63.4.304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68207913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}