82nd EAGE Annual Conference & Exhibition最新文献

{"title":"Development of Ultra-Deep Sparse Seismic Source Technology for 4D Reservoir Monitoring","authors":"P. Dhelie, V. Danielsen","doi":"10.3997/2214-4609.202112791","DOIUrl":"https://doi.org/10.3997/2214-4609.202112791","url":null,"abstract":"","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116996406","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":"Deep-LSRTM: least-squares reverse time migration via learned projection operators","authors":"K. T. Bautista, M. Sacchi","doi":"10.3997/2214-4609.202112723","DOIUrl":"https://doi.org/10.3997/2214-4609.202112723","url":null,"abstract":"","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114721275","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":"Data-driven prestack simultaneous inversion for impedance and gas saturation","authors":"W. Sang, S. Yuan, Z. Song, S. Chen, S. Wang","doi":"10.3997/2214-4609.202112788","DOIUrl":"https://doi.org/10.3997/2214-4609.202112788","url":null,"abstract":"","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125471798","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":"Multiscale core-seismic analysis for karst characterisation of MX field, Central Luconia Province, Offshore Sarawak, Malaysia.","authors":"S. Rahman, M. Husen, Grisel Jimenez Soto, S. B. Bing, M. Poppelreiter, N. Jamin, R. Rosli","doi":"10.3997/2214-4609.202112737","DOIUrl":"https://doi.org/10.3997/2214-4609.202112737","url":null,"abstract":"In the Oil and Gas industry, carbonate reservoirs play an important role in holding most of the world’s hydrocarbon reserves. They account for 60% of the world’s oil and 40% of the world’s gas reserves. Nonetheless, carbonate reservoirs continue to pose challenges due to their multi-scale heterogeneities from depositional and post-depositional physical and chemical diagenetic processes that alter the rock’s structure, affecting its porosity and permeability. The Middle Miocene Central Luconia province located in offshore Sarawak has received much attention since its discovery by Shell as one of Malaysia’s largest gas fields in 1971. Over 250 carbonate build-ups occur offshore in the Central Luconia province. Around 60 platforms have been drilled and almost every field has encountered indications for high permeability zones likely associated with karst such as mud losses and drill bit drops during drilling activities. Some fields were left abandoned due to the mud losses that could not be controlled. Geometries, distribution, and the dimension of karst in Central Luconia fields remain unknown. They have not been studied in detail. Due to the current lack of understanding of karst morphology, a regional integrated study has been initiated in this research to establish a systematic multidisciplinary approach and acquire detailed geological understanding from one-dimensional to three-dimensional aspects. The heterogeneity of carbonate reservoirs arises from the differences of porosity/permeability, which are marked, by diagenetic overprint and/or meteoric dissolution resulting in Reservoir Rock Types (RRTs) with different petrophysical properties (Jimenez et al., 2020).","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130807864","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":"Calculating the electrical conductivity of digital rocks based on two-dimensional images","authors":"H. Bao, T. Han","doi":"10.3997/2214-4609.202112748","DOIUrl":"https://doi.org/10.3997/2214-4609.202112748","url":null,"abstract":"","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133504599","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 novel method to correct the effect of the internal gradients on the NMR T2 spectrum","authors":"J. Liu, Y. Fan, X. Ge","doi":"10.3997/2214-4609.202112797","DOIUrl":"https://doi.org/10.3997/2214-4609.202112797","url":null,"abstract":"The laboratory nuclear magnetic resonance (NMR) measurement and NMR logging have been widely used in formation evaluation since the recorded signal is directly related with the hydrogen. However, the internal magnetic field gradient (G) that is generated from the magnetic susceptibility difference between pore fluid and solid grains, is an important factor that cannot be ignored. It affects the accuracy of NMR logging interpretation and results of fluid identification by accelerating the transverse relaxation of pore fluid through diffusion relaxation, especially in paramagnetic minerals reservoirs such as volcanic and shale.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128537877","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":"Equivalent Q Estimation Using a Deep-learning-based Decoupling Method","authors":"L. Xu, Z. Gao, S. Hu, C. Li, J. Gao","doi":"10.3997/2214-4609.202112720","DOIUrl":"https://doi.org/10.3997/2214-4609.202112720","url":null,"abstract":"In seismic exploration, Q model estimation from post-stack seismic data is an important problem since this model is a key prerequisite for reservoir identification and improving the vertical resolution of seismic data. The effects of Q are mainly manifested amplitude reduction and phase distortion of seismic data. In order to obtain high-resolution seismic data to describe oil and gas reservoirs, many Q factor estimation methods have been proposed. These methods can be roughly divided into direct estimation methods and inversion methods. Direct estimation methods, such as logarithmic spectral ratio method, frequency shift method, etc., usually use the attributes of seismic data to estimate Q, but it usually has disadvantages such as poor stability, dependence on source wavelet type, and the need for piecewise estimation of multi-layer Q model (Tonn, 1991). In contrast, the inversion methods regard Q factor as a model parameter and use the inversion algorithm to obtain the dynamic Q curve with traveltime or depth, which improves the calculation efficiency and stability, such as a novel method for Q analysis on reflection seismic data (Wang, 2004). However, the reflectivity and Q factor simultaneously affects the waveform of post-stack seismic data, leading to the fact that the Q model cannot be independently estimated without providing an accurate reflectivity model. The general approach for solving this problem is to simultaneously estimate these two parameters in an alternative iteration way (Wang et al., 2016). However, since neither the Q factor nor the reflectivity has a good initial model, the approach has no convergence guarantee.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126854713","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":"“Sharing Private” Multi-task Learning for Petrophysical Parameters Prediction with Logs","authors":"R. Shao, L. Xiao, G. Liao","doi":"10.3997/2214-4609.202112714","DOIUrl":"https://doi.org/10.3997/2214-4609.202112714","url":null,"abstract":"Using neural network to map the relation between logging data and petrophysical parameters has been studied actively in recent years (Korjani, 2016). The results show that neural network can predict petrophysical parameters based on logging data with higher efficiency and accuracy than traditional model-driven methods (Korjani, 2016). The existing study, however, single predict neural network were used, that is, for a neural network one petrophysical parameter can be predicted, such as porosity (POR) or water saturation (SW) with a set of logging data. We propose a multi-task machine learning method for petrophysical parameter prediction with logs, which can improve the efficiency, simplify the process and reduce the mean absolute error compared with single predict neural network.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115128554","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":"Deterministic Smart Tools to Predict Recovery Factor Performance of Saline Water Injection in Carbonated Reservoirs","authors":"A. Maghsoudian, A. Izadpanahi, A. Esfandiarian","doi":"10.3997/2214-4609.202112778","DOIUrl":"https://doi.org/10.3997/2214-4609.202112778","url":null,"abstract":"Throughout decades due to the scarcity of petroleum sources and weak performance of traditional waterflooding on increasing oil recovery factor (RF), enhanced oil recovery (EOR) processes have been applied to improve the ultimate oil recovery (Maghsoudian et al., 2020b). Hence, determining a suitable and cost-effective method to enhanced the ultimate recovery is still notable for developing oil fields, particularly in carbonated fields (Derkani et al., 2018). Among all EOR methods the role of low water salinity gets higher attention in numerous studies on both sandstone and carbonate reservoirs due to their potential advantages such as cost-effectiveness, simple preparation procedure, and appropriate stability (Kondori et al., 2020; Maghsoudian et al., 2020a). Low salinity (Losal) waterflooding prepared by diluting high salinity water containing various divalent and monovalent ions. According to previous studies, Losal has an immense impact on underlying mechanisms in petroleum reservoirs in order to change the wettability condition into water-wet state and dwindle the final residual oil (Liu and Wang, 2020). Besides, a large number of coreflooding tests analysis illustrated Losal has a great effect in both the secondary and the tertiary oil recovery process (Katende and Sagala, 2019). Experimental procedures commonly are time-consuming, high cost, and low accessibility. Therefore, applying artificial intelligence (AI) techniques as an alternative method to overcome the aforementioned barriers will be a suitable and trustworthy approach to predict objective function(s) and improve future practical research, in the absence of deep knowledge and related mathematical formulation to targeted procedures. The most well-known models based on AI are the artificial neural network (ANN), genetic algorithm (GA), neuro-fuzzy inference system (ANFIS), genetic programming (GP), least-squares support vector machine (LSSVM), etc (Li et al., 2020). These models have a great capability to develop precise and reliable output variables and have a low cost and swift computational procedure with high accuracy (Kondori et al., 2020). Despite numerous comprehensive research and data gathering in sandstone reservoirs, lack of sufficient comprehensive studies in carbonated reservoirs is still required due to the presence of harsh reservoir conditions such as heterogeneity, high temperature, and different physio-chemicals phenomenon (Hao et al., 2019). Based on the previous researches, precise studies on the effect of Losal waterflooding on recovery factor performance in carbonated reservoir by applying smart predictive models is still required. Thus, this paper purposed to cover this important gap by using practical deterministic models. The main purpose of this research is to introduce smart predictive tools such as ANN and multigene genetic programming (MGGP) for obtaining RF of LSWI process based on different parameters including porosity, permeability, temperature, in","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123675538","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":"Estimate optimal parameters of finite-difference scheme for wavefield modelling","authors":"X. Fang, F. Niu, G. Yao","doi":"10.3997/2214-4609.202112740","DOIUrl":"https://doi.org/10.3997/2214-4609.202112740","url":null,"abstract":"","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128251099","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}