Under-compaction and hydrocarbon generation are the main factors affecting pore pressure. The current seismic pore pressure prediction method is to obtain the overpressure trend by estimating the normal compaction trend (NCT) to predict the physical parameters during normal compaction and comparing the measured parameters. However, selecting a single parameter to indicate overpressure may cause insufficient consideration of factors such as hydrocarbon generation. Since hydrocarbon generation requires specific temperature and other conditions, we roughly divide the pore pressure into two parts: under-compaction in the early stage and hydrocarbon generation after reaching the hydrocarbon generation threshold. We propose a petrophysical model for estimating the normal compaction trend before hydrocarbon generation, modify the bulk modulus of the model, and use the bulk modulus method to calculate the pressure generated by under-compaction; the pressure is added to obtain the final pore pressure. In the shale gas work area in the Sichuan Basin, the prediction results are more in line with the actual situation, and the petrophysical analysis shows that the ratio of free hydrocarbon content and kerogen to water is the influencing factor indicating pore pressure. The practicality of the pore pressure prediction formula considering hydrocarbon generation in oil and gas sweet spots is illustrated through an example in the research area.
{"title":"Estimation of pore pressure considering hydrocarbon generation pressurization using Bayesian inversion","authors":"Jiale Zhang, Z. Zong, Kun Luo","doi":"10.1190/int-2022-0082.1","DOIUrl":"https://doi.org/10.1190/int-2022-0082.1","url":null,"abstract":"Under-compaction and hydrocarbon generation are the main factors affecting pore pressure. The current seismic pore pressure prediction method is to obtain the overpressure trend by estimating the normal compaction trend (NCT) to predict the physical parameters during normal compaction and comparing the measured parameters. However, selecting a single parameter to indicate overpressure may cause insufficient consideration of factors such as hydrocarbon generation. Since hydrocarbon generation requires specific temperature and other conditions, we roughly divide the pore pressure into two parts: under-compaction in the early stage and hydrocarbon generation after reaching the hydrocarbon generation threshold. We propose a petrophysical model for estimating the normal compaction trend before hydrocarbon generation, modify the bulk modulus of the model, and use the bulk modulus method to calculate the pressure generated by under-compaction; the pressure is added to obtain the final pore pressure. In the shale gas work area in the Sichuan Basin, the prediction results are more in line with the actual situation, and the petrophysical analysis shows that the ratio of free hydrocarbon content and kerogen to water is the influencing factor indicating pore pressure. The practicality of the pore pressure prediction formula considering hydrocarbon generation in oil and gas sweet spots is illustrated through an example in the research area.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47267353","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}
Identification and prediction of high-quality source rocks is the key to obtaining new resources in the exploration area of Cenozoic basins in offshore China. We investigate the seismic response and area of hydrocarbon source rocks based on seismic data, well curves, lithologic interpretation, and geochemical analysis. The target is the source rock development zone of the W Formation in a survey of the South China Sea. The results show that the seismic response of thick layer source rocks differ from surrounding rocks in the seismic profile (strong reflections with opposite polarity at the top and bottom and messy or chaotic reflections inside). Seismic reflections of interlayer source rocks have the characteristics of low frequency and continuous strong amplitude. The dominant frequency and maximum amplitude decrease as the number of mudstone layers increases. Through seismic petrophysical analysis, we have obtained three sensitive parameters of source rock in this survey: clay content, P-wave impedance, and elastic impedance. We use different classification methods to realize the classification and prediction of hydrocarbon source rocks, among which the Kernel Fisher Discriminant Analysis (KFDA) method is the best. The prediction results are consistent with the geological background, geochemical information, and well curves.
{"title":"Seismic response analysis and distribution prediction of source rocks in a survey of the South China Sea","authors":"Weihua Jia, Z. Zong, Hongchao Sun, T. Lan","doi":"10.1190/int-2022-0072.1","DOIUrl":"https://doi.org/10.1190/int-2022-0072.1","url":null,"abstract":"Identification and prediction of high-quality source rocks is the key to obtaining new resources in the exploration area of Cenozoic basins in offshore China. We investigate the seismic response and area of hydrocarbon source rocks based on seismic data, well curves, lithologic interpretation, and geochemical analysis. The target is the source rock development zone of the W Formation in a survey of the South China Sea. The results show that the seismic response of thick layer source rocks differ from surrounding rocks in the seismic profile (strong reflections with opposite polarity at the top and bottom and messy or chaotic reflections inside). Seismic reflections of interlayer source rocks have the characteristics of low frequency and continuous strong amplitude. The dominant frequency and maximum amplitude decrease as the number of mudstone layers increases. Through seismic petrophysical analysis, we have obtained three sensitive parameters of source rock in this survey: clay content, P-wave impedance, and elastic impedance. We use different classification methods to realize the classification and prediction of hydrocarbon source rocks, among which the Kernel Fisher Discriminant Analysis (KFDA) method is the best. The prediction results are consistent with the geological background, geochemical information, and well curves.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46794155","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}
Numerous studies have demonstrated the capability of supervised deep learning techniques for predicting geologic features of interest from seismic sections, including features that are difficult to identify using traditional interpretation methods. However, the successful application of these techniques in practice has been limited by the difficulty of obtaining a large training data set where the seismic data and corresponding ground truth labels are well-defined. Manually creating large amounts of labels requires a heavy workload, and the uncertainty of the interpretation and labeling process decreases the model’s ability for making accurate predictions. Using the chalk-flint sequence scenario onshore Denmark as an example, we have developed a novel workflow for predicting subresolution thin layers from seismic sections. It entails generating large quantities of synthetic training data with high-quality labels using stochastic geologic modeling, training a convolutional neural network based on the synthetic data set, and applying it to real seismic data. This is, to our knowledge, the first example of using deep learning to predict subresolution thin layers from seismic data based on geostatistically generated training images. It is shown that a neural network trained on synthetic data can predict a realistic number of subresolution flint layers from the real seismic data that have been collected from the Stevns region in Denmark, which has value for the understanding of the overall geologic characteristics of succession and engineering applications such as construction site evaluation.
{"title":"USING SYNTHETIC DATA TRAINED CONVOLUTIONAL NEURAL NETWORK FOR PREDICTING SUB-RESOLUTION THIN LAYERS FROM SEISMIC DATA","authors":"Dongfang Qu, K. Mosegaard, R. Feng, L. Nielsen","doi":"10.1190/int-2022-0059.1","DOIUrl":"https://doi.org/10.1190/int-2022-0059.1","url":null,"abstract":"Numerous studies have demonstrated the capability of supervised deep learning techniques for predicting geologic features of interest from seismic sections, including features that are difficult to identify using traditional interpretation methods. However, the successful application of these techniques in practice has been limited by the difficulty of obtaining a large training data set where the seismic data and corresponding ground truth labels are well-defined. Manually creating large amounts of labels requires a heavy workload, and the uncertainty of the interpretation and labeling process decreases the model’s ability for making accurate predictions. Using the chalk-flint sequence scenario onshore Denmark as an example, we have developed a novel workflow for predicting subresolution thin layers from seismic sections. It entails generating large quantities of synthetic training data with high-quality labels using stochastic geologic modeling, training a convolutional neural network based on the synthetic data set, and applying it to real seismic data. This is, to our knowledge, the first example of using deep learning to predict subresolution thin layers from seismic data based on geostatistically generated training images. It is shown that a neural network trained on synthetic data can predict a realistic number of subresolution flint layers from the real seismic data that have been collected from the Stevns region in Denmark, which has value for the understanding of the overall geologic characteristics of succession and engineering applications such as construction site evaluation.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44509225","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}
Pub Date : 2023-02-01DOI: 10.1190/int-2023-0209-bm.1
{"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":" ","pages":""},"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}
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.
{"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":" ","pages":""},"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}
A large-rimmed carbonate platform was developed in the Amu Darya Basin during the middlelate Jurassic CallovianOxfordian 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 CallovianOxfordian carbonate rocks in the northern Amu Darya Basin during the middlelate 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.
{"title":"Characteristics and Genesis of reef-bank complexes in deep shelf Facies: A Case Study of MiddleLate 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 middlelate Jurassic CallovianOxfordian 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 CallovianOxfordian carbonate rocks in the northern Amu Darya Basin during the middlelate 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":" ","pages":""},"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}
Pub Date : 2023-01-17DOI: 10.1190/int-2023-0112-fe.1
V. Egorov, D. Dunlap, S. Amoyedo, I. Filina, J. Gharib, O. Davogustto, B. Németh
{"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":" ","pages":""},"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}
Tingshang Yan, Yongshou Dai, Yong Wan, Weifeng Sun, Haoyu Han
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.
{"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":" ","pages":""},"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}
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.
{"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":" ","pages":""},"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}
D. Duarte, Rafael Pires de Lima, J. Tellez, M. Pranter
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.
Sho Vel Tum油田的Sycamore组主要由富含粘土的泥岩和富含石英的粉砂岩组成。富含粘土的泥岩主要由粘土、石英颗粒、一些外来物和碎屑有机物组成。粉砂岩无结构，分为两个岩相：高孔渗块状钙质粉砂岩（MCSt）和低孔渗块状方解石胶结粉砂岩（MCcSt）。岩心和测井数据提供了矿物学、岩性和孔隙度信息，这些信息有助于定义岩石物理相（岩相）并在Sycamore地层内创建相测井。我们利用这些数据建立了Sycamore组的地层结构，并绘制了其空间变异性和储层性质图。为了对无芯井中的Sycamore组岩相进行分类，我们开发了一种基于机器学习的工作流程，该工作流程比较了1800多个分类模型，并选择测井曲线、算法和超参数的最佳组合来预测已定义的岩相。该过程包括在四种分类算法中优化的测井组合：人工神经网络（ANN）、K-最近邻（KNN）、支持向量机（SVM）和随机森林（RF）。为了调整每个分类器，我们使用网格搜索和5倍交叉验证来找到三个超参数的最佳组合，以改进每个算法的结果。该工作流程允许以低成本高效地从核心提取信息。在我们在无芯井中生成岩相测井后，我们将它们与多个约束条件相结合，创建了Sho Vel Tum油田Sycamore组的三维岩相模型，并分析了对岩相的地层和成岩控制及其对储层质量的影响。
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