A Stochastic Object Model Conditioned to High-Quality Seismic Data

The Incorporated Research Institutions for Seismology (IRIS) Seismographs in Schools (SIS) program has supported teachers in the use of educational seismometers and real-time seismic data in the classroom for the past 20 years. To better understand how the seismographs and seismic data are being used in the classroom, we sent a survey to 770 past or current program participants. The survey asked about their seismometer, seismic data use, software use, the impact of seismic data on their classroom, and what additional seismology-related resources or instruction they provide to their students. Four highly-engaged teachers were then recruited from this larger pool for a case study. The purpose of this study was to better understand these highly-engaged teachers, their experiences with seismic instrumentation, software and data in the classroom, how and to what degree seismic data has impacted their curriculum and instruction, and their perceptions of the impact of the SIS program on their students’ understanding of Earth Science concepts. The case study results show that each of the highly-engaged teachers values data driven instruction, instrument science, and the integration of seismic data into the classroom more than just during their earthquake units. They have made it part of everyday class activities by having students be aware of data coming in, noting earthquakes in the news, and helping students learn more about analyzing data for advanced investigations. While there were differences in their implementations, in all cases a critical feature of their engagement with students was use of a variety of seismology-related resources which connected the seismic data to the rest of the curriculum. Thus, the use of seismic data was just one component of their seismology-related teaching. This study also highlights the value of a local sensor, as all four highly-engaged teachers stressed that students as stewards of the seismometer linked them to the science in an engaging and dynamic way. Thus, while the highly-engaged teachers had a primary responsibility to promote learning, their focus on student engagement is also helping to create young citizen scientists.

The aim of the study is to condition stochastic generated realizations on well test data in order to improve simulation of fades and petrophysics in fluvial reservoirs. First we have used the pressure data to estimate the shortest distance from the well to a possible channel boundary and thereby simulate the channel structures. The well test also provides the permeability average in the part of the channel intersected by the well. Together with core/log data and general knowledge of the reservoir this have been used to simulate permeability. These permeability realizations is input to a numerical flow simulator and compared with experimental results of the well test.

Seismic structural attributes (e.g., curvatures and similarity), which are usually extracted from post-stack seismic data, have been been widely used to help seismic interpretation. Because seismic data is band limited and contaminated with noise, which results in low resolution of the extracted attribute outputs. This leads to the limited use of the structural attributes mostly visualized on a time or horizon slice but not very useful at vertical cross-sections. To address this, we will present an application of three types of seismic attributes (most positive curvature, most negative curvature and similarity) on a spiky reflectivity cube, which is derived from post-stack seismic data by using a sparse layer inversion with basis pursuit technique. The attributes calculated from the reflectivity cube show higher resolution results in both vertical and horizontal views, which can be used as a new attribute to help seismic interpretation.

We propose a two-stage approach to integrating seismic data into reservoir characterization. First, we use a non-parametric approach to calibrate the seismic and well data through an optimal transformation to obtain the maximal correlation between two data sets. These optimal transformations are totally data-driven and do not assume any a priori functional relationship. Next, cokriging or stochastic cosimulation is carried out in the transformed space to generate conditional realizations of reservoir properties. The proposed approach allows for non-linearity between reservoir properties and seismic attributes and exploits the secondary data to its fullest potential. Furthermore, cokriging or cosimulation is considerably simplified when carried in conjunction with the optimal transformations because of a significant reduction in the variance function calculations particularly when multiple seismic attributes are involved. The proposed approach has been applied to synthetic as well as field examples. The synthetic examples involve reproducing a pre-generated primary data set using sparse primary and multiple dense secondary data sets. A comparison with traditional kriging and cokriging is also presented to illustrate the superiority of our proposed approach. The field example uses 3-D seismic and well log data from a 2 mi2 area of the Stratton gas field in South Texas — a fluvial reservoir system. Using multiple seismic attributes in conjunction with well data, we estimate pore-footage distribution for a selected zone in the middle Frio formation. Introduction It is well-recognized that integration of seismic data into reservoir characterization can play a significant role in reducing uncertainties in interwell reservoir properties. However, use of seismic data in reservoir characterization still remains rather limited primarily due to the inexact nature of the relationship between seismic and reservoir properties. Many seismic characteristics exhibit complicated effects of reservoir parameters such as lithology, petrophysics and fluid content. Hence, the link between seismic and reservoir properties is often non-unique, multivariate, and non-linear. Currently, there are two common approaches for integrating seismic data during reservoir characterization. The first approach involves inversion of seismic data to obtain seismic velocity distribution and then generating reservoir properties either using empirical models or through data calibration with existing wells. This approach is rather straight-forward and easy to implement. However, when applied to reservoirs complicated by large variations in lithology, fluid saturation, and other petrological factors, the inverted seismic velocity alone may not be sufficient to characterize reservoir properties with confidence. The second approach is more statistical in nature. It involves extraction of various seismic attributes from the formation under consideration and then estimating reservoir properties using multivariate statistical correlation or pattern, recognition algorithms. This approach, although not as direct as the first approach, can incorporate as many seismic attributes as might be available and thus is able to deal with more complicated reservoirs. However, traditional stochastic cosimulation techniques are not suitable for cases where multiple seismic attributes are involved. P. 37

Previous No AccessProceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021Enhancing seismic fault extraction through multiple ant tracking on cloud environmentAuthors: Mohamed AmroucheAyako OtakaraKwangho LeeMisa OkadaMohamed AmroucheSchlumbergerSearch for more papers by this author, Ayako OtakaraSchlumbergerSearch for more papers by this author, Kwangho LeeSchlumbergerSearch for more papers by this author, and Misa OkadaSchlumbergerSearch for more papers by this authorhttps://doi.org/10.1190/segj2021-091.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Manual seismic faults interpretation can be one of the most time-consuming tasks in reservoir modeling. To accelerate the fault extraction process, some existing methods based on seismic attributes analysis such as the Ant Tracking method are already well adapted by experienced interpreters. The Ant Tracking algorithm is an innovative workflow that introduces an original paradigm in fault interpretation, by emulating the behavior of ant colonies in nature to track and extract faults and fractures network from the seismic data. This method has already showed good results on improving seismic interpretation workflows. However, some of the limitations of the ant tracking remain the range of uncertainty on properly mapping the fault continuity in low quality seismic data, and deeper structures where the seismic noise is more prominent. The Ant Tracking process also requires high spec hardware to properly perform, specially for large 3D seismic volumes. In this study, we proposed a new approach for Ant Tracking interpretation by averaging multiple estimations of the seismic data using different seismic attributes and parameters tunings. Our results show that the proposed combined and iterative strategy enables to effectively reduce the noise level in the seismic data and improve the continuity of the faults network, taking advantage of the powerful cloud environment to enhance the computation speed. Keywords: seismic interpretation, ant tracking, automated fault extraction, seismic attributesPermalink: https://doi.org/10.1190/segj2021-091.1FiguresReferencesRelatedDetails Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021ISSN (online):2159-6832Copyright: 2021 Pages: 349 publication data© 2021 Published in electronic format with permission by the Society of Exploration Geophysicists of JapanPublisher:Society of Exploration GeophysicistsSociety of Exploration Geophysicists of Japan HistoryPublished Online: 29 Nov 2021 CITATION INFORMATION Mohamed Amrouche, Ayako Otakara, Kwangho Lee, and Misa Okada, (2021), "Enhancing seismic fault extraction through multiple ant tracking on cloud environment," SEG Global Meeting Abstracts : 346-349. https://doi.org/10.1190/segj2021-091.1 Plain-Language Summary Keywordsseismic interpretationant trackingautomated fault extractionseismic attributesPDF DownloadLoading ...

Seismic Petrophysics in Quantitative Interpretation is written for oil and gas industry professionals and academicians who are concerned with the use of seismic data in petroleum exploration and production. In addition, it should prove useful toward thoughtful applications of those data by geotechnical engineers. Seismic interpretation can be made simple and robust by integrating rock-physics principles with the seismic and petrophysical attributes that bear on the properties of conventional reservoirs (thickness, net/gross, lithology, porosity, permeability, and satu-ration) and of unconventional reservoirs (thickness, lithology, organic richness, and thermal maturity). Practical solutions can be used to address existing interpretation problems in rock-physics-based AVO analysis and prestack seismic inversion in order to streamline the workflows in subsurface characterization.

Deep sand distributions are a vital component of reservoir characterization and the generation of successful reservoir prediction. Through the integration of selected seismic attributes using broadband seismic data. The reservoir distributions and architectures are accurately delineate the deep Paleozoic reservoir in IO gas field. Seismic interpretation on broadband data helping in making 3D structural modeling generate a regionally accurate image of the subsurface structures at low relief three way closure on the foot wall down thrown block. Seismic attributes like (second derivative and RMS Amplitude) could indicate that Paleozoic reservoir is characterize by the presence of discrete sand body (sand-filled channel). Seismic attribute provided an extensive vertical and lateral information set for the reservoir heterogeneity between wells; it was utilized as the information requirement in prediction of lithofacies distribution. The lithofacies obtained from integrating seismic attributes from seismic broadband data are calibrated with facies identified using well logs. The lithofacies model derived from this integrated approach provides an additional geologic insight into the stratigraphic architecture and reservoir distribution and lessens exploration/development risk in the IO gas field. The IO discovery gives us a good justification to follow this well with MAAT N-1X ST3 well to reveal the sand distribution in this area (recent extraordinary success gas/condensate discovery) and it will open new opportunities that have similar phenomena. The resulting workflow is an effective tool for highlighting potential areas of better quality reservoir development, thereby demonstrating the value of this integrated approach

The development of seismic technology has made seismic data to be widely used in the interpretation of stratigraphic sequence frames, reservoir identification, fluid detection, and other research fields involved in reservoir description. The 3D technology reservoirs have always been the focus, as well as difficulty, of research. With the rapid development of information technology and the continuous improvement of seismic exploration level, people have put forward higher requirements for the accuracy of seismic data interpretation results. Aiming at the large number of structural and unstructured data in seismic, logging, geology, and other disciplines involved in seismic interpretation, how to effectively organize and coordinate analysis to discover the hidden reservoir structure and oil and gas distribution information has always been a geological and important topic for information processing technicians. This thesis is aimed at the current high-water-phase development of Shengtuo Oilfield reservoir and the problems existing in geological research. Based on seismic structural interpretation and attribute analysis, this paper analyzes the reservoir structural characteristics, sedimentary characteristics, and reservoir physical parameter characteristics based on geology, logging interpretation, core analysis, drilling, and seismic interpretation. Using the kriging method with external drift can cooperate with seismic variables to establish a reservoir geological model to study the Shengtuo Oilfield reservoir. We combine artificial intelligence technology with geological modeling technology of seismic interpretation results to explore the best method for predicting earthquakes. The research results in this paper show that the relative error of the model established by the kriging method in the article is relatively small for thinning wells, mainly concentrated around 1%. Examination of the thinning wells of 45 wells shows that the model established is basically good and the example has high accuracy. The research results in this paper have a guiding study of distribution and tapping potentials in the study area, formulating reasonable development and adjustment plans and improving oil recovery.

Interpretation of geologic structures entails ambiguity and uncertainties. It usually requires interpreter judgment and is time consuming. Deep exploitation of resources challenges the accuracy and efficiency of geologic structure interpretation. The application of machine-learning algorithms to seismic interpretation can effectively solve these problems. We analyzed the theory and applicability of five machine-learning algorithms. Seismic forward modeling is a key connection between the model and seismic response, and it can obtain seismic data of known geologic structures. Based on the modeling data, we first optimized the seismic attributes sensitive to the target geologic structure and then we verified the accuracy of the five machine-learning algorithms by the cross-checking method. In this case, the random forest algorithm had the highest accuracy. So we examined the structural interpretation method based on a random forest using the 3D seismic reflection data from coalfield exploration. The prediction effect of this interpretation workflow is verified by comparison with known geologic structures on the plane and profile. The results suggest that the random forest algorithm is feasible to indicate geologic structure interpretations in the case of collapsed column and fault structures and it can effectively improve the efficiency of seismic interpretation and its accuracy. The machine-learning-based workflow provides a new technique for seismic structure interpretation in coal mining.

Seismic is one of the main methods used for the identification of structures and stratigraphic studies in sedimentary basins. In the Santos Basin, numerous 2D and 3D seismic surveys are being conducted in order to get a better ima-ge of the geological section to depths beyond the base of salt layer. Crustal modelling studies that make joint interpretation of seismic and gravity data are found in the literature, however there are few studies that relate gravity anomalies directly to salt structures. This work aims to associate gravity anomalies with salt structures from seismic and gravimetric interpretation. For studies aimed to model the crustal structure from gravity field data, the knowledge of two major discontinuities is required, the basement and the Moho. Such interfaces are often not easily seen by seismic and so, during this study, they were found by different methods involving analysis of gravity anomalies. The other interfaces involving density contrasts were analyzed based on seismic interpretation. The results showed that the obtained seismic geological interpretations may provide additional information when compared to gravity anomaly data. In all the modelled profiles, some geological information of the Santos Basin that are not visible in the seismic, could be interpreted according to the geological model and the adjustment of gravity anomaly curves. As a final conclusion of this work, it is suggested that the combined analysis of the two cited geophysical methods, can provide important information about the crustal structure and to assist in modelling the salt layer.

Seismic is one of the main methods used for the identification of structures and stratigraphic studies in sedimentary basins. In the Santos Basin, numerous 2D and 3D seismic surveys are being conducted in order to get a better ima-ge of the geological section to depths beyond the base of salt layer. Crustal modelling studies that make joint interpretation of seismic and gravity data are found in the literature, however there are few studies that relate gravity anomalies directly to salt structures. This work aims to associate gravity anomalies with salt structures from seismic and gravimetric interpretation. For studies aimed to model the crustal structure from gravity field data, the knowledge of two major discontinuities is required, the basement and the Moho. Such interfaces are often not easily seen by seismic and so, during this study, they were found by different methods involving analysis of gravity anomalies. The other interfaces involving density contrasts were analyzed based on seismic interpretation. The results showed that the obtained seismic geological interpretations may provide additional information when compared to gravity anomaly data. In all the modelled profiles, some geological information of the Santos Basin that are not visible in the seismic, could be interpreted according to the geological model and the adjustment of gravity anomaly curves. As a final conclusion of this work, it is suggested that the combined analysis of the two cited geophysical methods, can provide important information about the crustal structure and to assist in modelling the salt layer.

Sand-lithofacies distributions are a vital component of reservoir characterisation and the generation of successful reservoir models, particularly in thin reservoirs thickness. The integration between seismic attributes and conceptual geologic models is important to identify the sand distributions and reservoir geometries. The aim of this work is illustrating the uses of seismic data in revealing the structural and stratigraphic elements of two main reservoir units. It is important to identify and list the attributes, which can be involved in analysing the reservoirs and their degree of involvement in the characterisation of the hydrocarbon accumulation zones. The study was carried out for the reservoirs: Abu Roash C and Abu Roach E in Sitra oil and gas concession, which is located in the northern part of the Egyptian Western Desert. Four seismic attributes are used: 1- Coherence (Variance) 2- 3D Curvature attributes were used for resolving mainly the structural features and also the stratigraphic features 3- Instantaneous phase 4- Instantaneous frequency attributes were used mainly for resolving the stratigraphic features. Through the integration of selected seismic attributes with post-stack seismic data, reservoir distributions and architectures are accurately delineated for the Upper Cretaceous reservoir (Abu Roash ‘C’ and ‘E’). Five wells were used to match the results of the seismic attributes and establish the regional trends of reservoir parameters. The integration of wells data, conventional seismic interpretation and extracted the relevant seismic attributes help in reaching a robust geologic model for the reservoirs of interest. Stratigraphic and structural interpretation objectives based on extracting the seismic attributes for reservoir characterisation become more dominant, as they go deeper in seismic data, to derive more subsurface geologic data than traditional interpretation. 3D structure model of the concern area was established by interpreting the available seismic data as both reservoirs implicated by two fault trends: NW-SE and ENE-WSW. By the end a 3D geological model was defined based on the results of the conventional interpretation of the seismic and seismic attributes to reflect all structural and stratigraphic elements of the two reservoir units.

Permanent Reservoir Monitoring (PRM) in systems deep-water settings provide on-demand snapshots for hydrocarbon reservoirs at different times during their production history. Delays in the interpretation turn-around of 4D seismic data reduce some benefits of the PRM. These delays could adversely impact the decision making processes despite obtaining information on demand. Using fast-track approaches in 4D seismic interpretation can provide timely information for reservoir management. This work focuses on a fast-track 4D seismic qualitative interpretation in PRM environment, with the aim of choosing the best seismic amplitude attribute (4D) to use. Different seismic attributes are extracted and the one with high signal-to-noise ratio is selected to carry out the 4D qualitative interpretation. All 4D signals are juxtaposed with well production history data to increase confidence in our interpretation. The selected attribute can be interpreted and used for the foreseeable life of field. This workflow has been developed and applied on post-salt Brazilian offshore field to choose the best seismic attribute to conduct the 4D seismic qualitative interpretation.

Interpretation of gas chimney from seismic data using artificial neural network: A study from Maari 3D prospect in the Taranaki basin, New Zealand

Before undertaking any seismic interpretation project, it is essential to understand the rock physics of the area so that the interpreter knows what to look for in their seismic. Rock physics modelling can be used to predict the effects of lithology, fluid, porosity, depth and sand thickness on the seismic response. It can also help to understand whether direct hydrocarbon indicators (DHIs) such as flat spots, bright spots, dim spots or polarity reversals are expected and also the best seismic attributes to use to identify hydrocarbons. Well ties can be used to determine the quality of the seismic data and infer how reliably predictions based on the rock physics can be applied back to the seismic data. The rock physics of several producing assets across the Gulf of Thailand have been examined and compared. Rock properties of over 200 wells have been analyzed based on stratigraphy, lithology, fluid fill, facies, porosity and depth. It was observed that the rock physics could be split into two distinct regimes based upon depositional environment separated by a key unconformity. The first rock physics regime is observed in the shallow high porosity fluvial systems found within the Nong Yao Field and the younger reservoirs of the Jasmine and Manora Fields. In this regime, seismic attributes and seismic inversion have been successfully used to predict sand presence and thickness. Sands and shales have distinctly different rock properties and sands produce a strong class III-IV Amplitude Varation with Offset (AVO) response. Fluid effects can be observed on seismic although it is difficult as thickness and porosity changes may be incorrectly interpreted as fluid changes. The second rock physics regime is observed in the older, lower porosity lacustrine system found within the main reservoirs of the Manora Field and the pre-MMU reservoirs of the Jasmine Field. Within this regime, lithology prediction is much more difficult as sands and shales have very similar properties. Sands are modelled to have a class I-II AVO response and fluid effects are minimal. Understanding the rock physics of these fields allows future work to be focused appropriately. At the Nong Yao Field and in the younger section of the Jasmine Field, technical work is focused on seismic inversion, seismic attribute analysis and other geophysical techniques for lithology and fluid prediction. Whereas at the Manora Field and in the older reservoirs of the Jasmine Field, technical work is focused more on structural mapping and geological modelling, and amplitude analysis is of lower priority.