Interpretation-A Journal of Subsurface Characterization最新文献

{"title":"In appreciation of reviewers and editors","authors":"","doi":"10.1190/int-2023-0209-bm.1","DOIUrl":"https://doi.org/10.1190/int-2023-0209-bm.1","url":null,"abstract":"","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48559766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying AVO uncertainties related to calcite-cemented beds using Monte Carlo simulation","authors":"S. Tschache, V. Vinje, J. Lie, E. Iversen","doi":"10.1190/int-2022-0084.1","DOIUrl":"https://doi.org/10.1190/int-2022-0084.1","url":null,"abstract":"Calcite cement often occurs locally forming thin layers of calcite-cemented sandstone characterized by high seismic velocities and densities. Because of their strong impedance contrast with the surrounding rock, calcite-cemented intervals produce detectable seismic reflection signals that may interfere with target reflections at the top of a reservoir. In this case, the amplitude variation with offset (AVO) of the effective seismic signature will be altered and may even create a false hydrocarbon indication. From Monte Carlo simulation, we find that the presence of thin calcite-cemented beds increases the uncertainty of Bayesian pore-fluid classification based on the AVO attributes intercept and gradient. In the case example of a North Sea turbiditic oil and gas field, the probability of a false positive hydrocarbon indication increases from 3–5% to 18–21% assuming an equal probability of the occurrence of brine, oil, and gas. The results confirm that calcite-cemented beds can create a pitfall in AVO analysis. Realistic estimates of AVO uncertainty are crucial for the risk assessment of well placement decisions.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42862872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics and Genesis of reef-bank complexes in deep shelf Facies: A Case Study of Middle–Late Jurassic in the Northern Amu Darya Basin, Central Asia","authors":"Liangjie Zhang, Bingsong Yu, Hongjun Wang, Lingzhi Jiang, Xinglin Gong, Yuzhong Xing, Hongxi Li, Ming Li, Haidong Shi, Peng-yu Chen","doi":"10.1190/int-2022-0055.1","DOIUrl":"https://doi.org/10.1190/int-2022-0055.1","url":null,"abstract":"A large-rimmed carbonate platform was developed in the Amu Darya Basin during the middle–late Jurassic Callovian–Oxfordian period. What distinguishes it from typical carbonate platforms is that a series of reef-bank complexes were extensively developed in the deep shelf sedimentary zone of the basin. However, only a few studies have reported on the classification, characteristics, and genesis of these reef-bank complexes in relatively deep water, greatly limiting the development of deep-water carbonate sedimentology. To address this issue, the types and the genesis of reef-bank complexes in the deep shelf environment have been clarified based on the systematic petrography, seismic sedimentology, and geomorphology study of Callovian–Oxfordian carbonate rocks in the northern Amu Darya Basin during the middle–late Jurassic period. The results show that the reef-bank complexes are widely distributed in the deep shelf environment in the study area with laminar, reticulated, and zonal distributions. The reef-bank complexes include barrier-bonding reef-bank complexes, lime-mud mounds, and granular shoals. The deep shelf environment can be further divided into inner shelf, shelf margin, and shelf slope. The inner shelf and shelf margin have relatively shallow water body and a strong sedimentary hydrodynamic force, mainly developing reticulated reef-bank complexes and laminar granular shoals, whereas the shelf slope mostly developing zonal lime-mud mound deposits in strips. The scale of the reef-bank complexes is mainly controlled by basement paleogeomorphology and water energy. Relatively high-energy reef-bank complex bodies are developed on the seaward side of the paleo-uplift limb with relatively turbulent hydrodynamic conditions, while low-energy lime-mud mounds are mostly developed on the high position of paleo-uplift and landward side. The obtained findings can deepen our understanding of relatively deep-water carbonate sedimentation and enrich the carbonate sedimentation theory.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44745922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Organizing a special section","authors":"V. Egorov, D. Dunlap, S. Amoyedo, I. Filina, J. Gharib, O. Davogustto, B. Németh","doi":"10.1190/int-2023-0112-fe.1","DOIUrl":"https://doi.org/10.1190/int-2023-0112-fe.1","url":null,"abstract":"<jats:p> </jats:p>","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42340331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic spatially-variant noise suppression method in Tarim Basin based on FFDNet and Transfer Learning","authors":"Tingshang Yan, Yongshou Dai, Yong Wan, Weifeng Sun, Haoyu Han","doi":"10.1190/int-2022-0041.1","DOIUrl":"https://doi.org/10.1190/int-2022-0041.1","url":null,"abstract":"Due to the complex geological structure and ultra-deep reservoir location, the noise distribution of prestack seismic data in the Tarim Basin is non-uniform. However, most of the current seismic random noise suppression methods lack the flexibility to deal with spatially-variant random noise. To address this issue, we propose an intelligent denoising method for seismic spatially-variant random noise and apply it in the Tarim Basin. On the basis of DnCNN, we add an extra channel to the input and introduce a tunable noise level map as input. The noise level map has the same dimensions as the input noisy seismic data, and each element in the noise level map corresponds to a denoising level. By adjusting the noise level map, a single model is able to handle noise with different levels as well as spatially-variant noise. Due to the lack of labeled field data in the Tarim Basin, we introduce a transfer learning scheme that transfers features of effective signals learned from synthetic data to the denoiser for field data. The network learns the general and invariant features of effective signal from a large number of easily obtained synthetic data and then learns the real effective signal characteristics from a small amount of approximately clean field data in the target area by fine-tuning. The processing results of synthetic and field data demonstrate that compared with f-x deconvolution, Dictionary Learning and DnCNN, the proposed method exhibits high effectiveness in suppressing spatially-variant random noise and preserves the effective signals better.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41606589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multiple Point Stochastic Based Turbidite Lobe Architecture Geo-Modeling: A Case Study from L Oilfield, Lower Congo Basin, West Africa","authors":"Rui Xu, Wenbiao Zhang, Meng Li, Wenming Lu","doi":"10.1190/int-2022-0026.1","DOIUrl":"https://doi.org/10.1190/int-2022-0026.1","url":null,"abstract":"AAs a prominent component of turbidite deposition systems, turbidite lobe internal architecture characterization has proven essential due to its complicated sedimentary hierarchy and evident heterogeneity. This article demonstrates an integrated methodology for doing multiple point stochastic (MPS) simulation of deep-water turbidite single lobe architecture. Based on logging data, high-frequency seismic data, thorough architectural features analysis and 3D training image, MPS based geological modelling of Miocene turbidite lobe reservoir in Lower Congo Basin are carried out. This effort has two objectives: (1) to expand the geological knowledge base of deep-water turbidite lobes with morphology parameters and (2) to develop a process of turbidite geo-modelling that could characterize the architectural hierarchy of a single lobe with limited hard data. As a first step, we analyze and characterize properties of single lobe elements characteristics and the manner of sedimentary dispersion using 145-meter-long cores, well logging, and seismic analysis. Second, shallow seismic-based turbidites lobes pick-up and measurements to collect quantitative characteristics of turbidite lobes morphology has been conducted and will be used as geo-modelling guidance. Thirdly, a 3D lobe complex training image with single lobe architecture elements superposition is derived by seismic geo-body caving (using threshold truncation) and enhanced based on sedimentary distribution mode. MPS simulation incorporating well data, morphological parameters, training image and seismic inversion constraint is then performed, resulting in an architecture model that could describe single lobes is obtained. The simulation results generally followed the lobe architecture elements morphology and superposition. The coincidence between the MPS simulated turbidites lobe complex architecture model and the posterior well that could reach up to 86%. The article gives a methodology for a case study that proved the implementation of single turbidite lobe architectural characterization using multiple point stochastics, and the recommended process could be applied to other fields.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46227426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial variability of petrofacies using supervised machine learning and geostatistical modeling: Sycamore Formation, Sho-Vel-Tum Field, Oklahoma, USA","authors":"D. Duarte, Rafael Pires de Lima, J. Tellez, M. Pranter","doi":"10.1190/int-2022-0064.1","DOIUrl":"https://doi.org/10.1190/int-2022-0064.1","url":null,"abstract":"The Sycamore Formation at Sho-Vel-Tum Field primarily consists of clay-rich mudstones and quartz-rich siltstones. The clay-rich mudstones are mainly composed of clays, quartz grains, some allochems and detrital organic matter. The siltstones are structureless and are divided into two petrofacies: high porosity and permeability massive calcareous siltstones (MCSt) and low porosity and permeability massive calcite-cemented siltstones (MCcSt). Core and well-log data provide mineralogical, lithological, and porosity information that is useful to define petrophysical facies (petrofacies) and to create facies logs within the Sycamore Formation. We used the data to establish the Sycamore Formation stratigraphic architecture and to map its spatial variability and reservoir properties. To classify the Sycamore Formation petrofacies in non-cored wells we developed a machine learning-based workflow that compares over 1,800 classification models and selects the best combination of well logs, algorithms, and hyperparameters to predict defined petrofacies. The process includes combinations of well logs that were optimized in four classification algorithms: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). To adjust each classifier, we used a Grid-Search and a 5-fold cross-validation to find the best combination of three hyperparameters to improve results of each algorithm. This workflow allows for the efficient extraction of information from cores at a low cost. After we generated petrofacies logs in non-cored wells, we combined them with multiple constraints to create a 3D petrofacies model for the Sycamore Formation at Sho-Vel-Tum Field and analyze the stratigraphic and diagenetic controls on petrofacies and its impact in reservoir quality.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42452947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation Model of Shale Adsorbed Gas Considering Clay and Water Saturation","authors":"Kun Liu, Jing Lu, Song Hu, Z. Nan","doi":"10.1190/int-2022-0066.1","DOIUrl":"https://doi.org/10.1190/int-2022-0066.1","url":null,"abstract":"Shale adsorption capacity is affected by many factors including temperature, pressure, geochemical characteristics of organic matter, clay, and water saturation. The traditional calculation model of adsorbed gas content only considers the influence of temperature, pressure, and organic geochemical characteristics. The influence of clay and water saturation on adsorption capacity is seldom considered. Isotherm adsorption experiments were conducted on synthetic specimens and natural specimens with varying clay types, clay contents, and water saturations. Then, the influences of clay and water saturation on the adsorption capacity were systematically studied. The experimental results found that the order of clay adsorption capacities was smectite > kaolinite > chlorite > illite. The multicomponent superposition rule was applicable in evaluating shale-adsorbed gas content. The total adsorption capacity was equal to the accumulation of the adsorption capacities of all types of clay and organic matter. Moisture will significantly reduce the adsorption capacity of shale. The adsorption capacities of synthetic specimens and natural specimens after being fully saturated with water were 9%–14% and 42%–61% of those in dry states, respectively. Then, a new shale-adsorbed gas evaluation model was established based on the Langmuir equation considering clay and water saturation. The calculation error of this new model was approximately 11%, which provides a new method for evaluating the adsorbed gas content of shale.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49112190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning framework for seismic facies classification","authors":"Harpreet Kaur, Nam Pham, Sergey Fomel, Zhicheng Geng, Luke Decker, Ben Gremillion, Michael Jervis, Raymond Abma, Shuang Gao","doi":"10.1190/int-2022-0048.1","DOIUrl":"https://doi.org/10.1190/int-2022-0048.1","url":null,"abstract":"We have proposed a deep neural network-based framework for seismic facies classification. We implement two different neural networks based on the architectures of DeepLabv3+ and generative adversarial network for segmentation and compare the mapping results from seismic reflection data to lithologic facies. DeepLabv3+ predictions have sharper boundaries between the predicted facies whereas generative adversarial network output has a better continuity of predicted facies. We incorporate uncertainty analysis into the workflow using a Bayesian framework. The proposed approach consisting of joint analysis of predicted facies from multiple networks along with uncertainty in prediction accelerates the interpretation process by reducing the need for human intervention and also lessens individual biases that an interpreter may bring. We determine the effectiveness of the proposed algorithm by testing on field data examples, and we find that the proposed workflow classifies facies accurately. This may potentially enable the development of depositional environment maps in areas of low well density.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134968762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Küresel Yapıda Grafitik Karbon Sentezi ve Karakterizasyonu","authors":"Fadime Ateş, Elif Tahtasakal, Selin Şahin Sevgili","doi":"10.29228/jchar.69035","DOIUrl":"https://doi.org/10.29228/jchar.69035","url":null,"abstract":"","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82939426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}