Artificial Intelligence in Geosciences最新文献

{"title":"Rapid identification of high-quality marine shale gas reservoirs based on the oversampling method and random forest algorithm","authors":"Linqi Zhu ,&nbsp;Xueqing Zhou ,&nbsp;Chaomo Zhang","doi":"10.1016/j.aiig.2021.12.001","DOIUrl":"10.1016/j.aiig.2021.12.001","url":null,"abstract":"<div><p>The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage. However, due to the serious nonlinear relationship between the logging curve response and high-quality reservoirs, the rapid identification of high-quality reservoirs has always been a problem of low accuracy. This study proposes a combination of the oversampling method and random forest algorithm to improve the identification accuracy of high-quality reservoirs based on logging data. The oversampling method is used to balance the number of samples of different types and the random forest algorithm is used to establish a high-precision and high-quality reservoir identification model. From the perspective of the prediction effect, the reservoir identification method that combines the oversampling method and the random forest algorithm has increased the accuracy of reservoir identification from the 44% seen in other machine learning algorithms to 78%, and the effect is significant. This research can improve the identifiability of high-quality marine shale gas reservoirs, guide the drilling of horizontal wells, and provide tangible help for the precise formulation of marine shale gas development plans.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 76-81"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544121000307/pdfft?md5=6df2bec460d6f3a4881187472d72e5ac&pid=1-s2.0-S2666544121000307-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79547585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing lithofacies machine learning predictions with gamma-ray attributes for boreholes with limited diversity of recorded well logs","authors":"David A. Wood","doi":"10.1016/j.aiig.2022.02.007","DOIUrl":"10.1016/j.aiig.2022.02.007","url":null,"abstract":"<div><p>Derivative and volatility attributes can be usefully calculated from recorded gamma ray (GR) data to enhance lithofacies classification in wellbores penetrating multiple lithologies. Such attributes extract information about the log curve shape that cannot be readily discerned from the recorded well log data. A logged wellbore section for which 8911 data records are available for the three recorded logs (GR, sonic (DT) and bulk density (PB)) is evaluated. That section demonstrates the value of the GR attributes for machine learning (ML) lithofacies predictions. Five feature selection configurations are considered. The 9-var configuration including GR, DT, PB and six GR attributes, and the 7-var configuration of GR and the six GR attributes, provide the most accurate and reproducible lithofacies predictions. The other three feature configurations evaluated do not include the GR attributes but just one to three of the recorded log features. The results of seven ML models and two regression models reveal that K-nearest neighbor (KNN), random forest (RF) and extreme gradient boosting (XGB) are the best performing models. They generate between 14 and 23 misclassification from 8911 data records for the 9-var model. Multi-layer perceptron (MLP) and support vector classification (SVC) do not perform well with the 7-var model which lacks the PB feature displaying the highest correlation with facies class. Annotated confusion matrices reveal that KNN, RF and XGB models can effectively distinguish all facies classes for the 9-var and 7-var configurations (that includes the GR attributes), whereas none of the models can achieve that outcome for the 3-var configuration (that excludes the GR attributes). Accurately distinguishing lithofacies using well-log data in sedimentary sections is an important objective in applied geoscience. The straightforward, GR-attribute method proposed works to improve confidence in ML-lithofacies classifications based on limited recorded well-log data.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 148-164"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000077/pdfft?md5=2bf3b12ae35a11a62a8a749a700d3504&pid=1-s2.0-S2666544122000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75671841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The potential of self-supervised networks for random noise suppression in seismic data","authors":"Claire Birnie,&nbsp;Matteo Ravasi,&nbsp;Sixiu Liu,&nbsp;Tariq Alkhalifah","doi":"10.1016/j.aiig.2021.11.001","DOIUrl":"10.1016/j.aiig.2021.11.001","url":null,"abstract":"<div><p>Noise suppression is an essential step in many seismic processing workflows. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural networks have been successfully used to denoise seismic data in a supervised fashion. However, supervised learning always comes with the often unachievable requirement of having noisy-clean data pairs for training. Using blind-spot networks, we redefine the denoising task as a self-supervised procedure where the network uses the surrounding noisy samples to estimate the noise-free value of a central sample. Based on the assumption that noise is statistically independent between samples, the network struggles to predict the noise component of the sample due to its randomicity, whilst the signal component is accurately predicted due to its spatio-temporal coherency. Illustrated on synthetic examples, the blind-spot network is shown to be an efficient denoiser of seismic data contaminated by random noise with minimal damage to the signal; therefore, providing improvements in both the image domain and down-the-line tasks, such as post-stack inversion. To conclude our study, the suggested approach is applied to field data and the results are compared with two commonly used random denoising techniques: FX-deconvolution and sparsity-promoting inversion by Curvelet transform. By demonstrating that blind-spot networks are an efficient suppressor of random noise, we believe this is just the beginning of utilising self-supervised learning in seismic applications.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 47-59"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544121000277/pdfft?md5=9093d31a779f759e23dc35802b713c2b&pid=1-s2.0-S2666544121000277-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83541914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Site suitability for Aromatic Rice cultivation by integrating Geo-spatial and Machine learning algorithms in Kaliyaganj C.D. block, India","authors":"Debabrata Sarkar (Research Scholar),&nbsp;Sunil Saha (Research Scholar),&nbsp;Manab Maitra B.Sc. in Geography,&nbsp;Prolay Mondal Ph.D. (Assistant Professor)","doi":"10.1016/j.aiig.2022.03.001","DOIUrl":"10.1016/j.aiig.2022.03.001","url":null,"abstract":"<div><p>The purpose of this work is to assess the soil fertility for Tulaipanji rice cultivation in Kaliyaganj C.D. Block using the Analytic Hierarchy Process (AHP) and Machine learning algorithms along with the field survey data and GIS. A total of 40 soil samples from Tulaipanji rice fields (from 0 to 40 ​cm depth) have been randomly collected for the analysis of the soil health condition. For the purpose of assigning ratings to the parameters, ten experts' opinions were taken into account. The final soil fertility map indicates that 18.01% of the land is in excellent health condition to support Tulaipanji cultivation. The artificial neural networks (ANN), support vector machine (SVM), and Bagging models-based suitability analysis was also done using geo-spatial and soil data for Tulaipanji cultivation. Nevertheless, the ANN is the more appropriate model for locational analysis of Tulaipanji cultivation. The ANN-based findings show that areas of 25.8% (77.89 sq. km) are excellent for growing Tulaipanji rice, about 22.01% (66.45 sq. km) are highly suitable, 19.84% (59.90 sq. km) are moderately suitable, 21.19% (63.97 sq. km) are low suitable and 11.16% (33.69 sq. km) are not suitable for Tulaipanji rice cultivation. The receiver operating characteristic (ROC) curve depicts that the applied models have a high degree of accuracy. This endeavour will aid much in the soil fertility and site suitability assessment that will aid local government officials, academics, and the framers, to utilize the lands in a scientific way.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 179-191"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000089/pdfft?md5=02fe57ee21f8fd2961fb07fb79723b38&pid=1-s2.0-S2666544122000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77222073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near-surface velocity inversion from Rayleigh wave dispersion curves based on a differential evolution simulated annealing algorithm","authors":"Yaojun Wang ,&nbsp;Hua Wang ,&nbsp;Xijun Wu ,&nbsp;Keyu Chen ,&nbsp;Sheng Liu ,&nbsp;Xiaodong Deng","doi":"10.1016/j.aiig.2021.10.001","DOIUrl":"10.1016/j.aiig.2021.10.001","url":null,"abstract":"<div><p>The utilization of urban underground space in a smart city requires an accurate understanding of the underground structure. As an effective technique, Rayleigh wave exploration can accurately obtain information on the subsurface. In particular, Rayleigh wave dispersion curves can be used to determine the near-surface shear-wave velocity structure. This is a typical multiparameter, high-dimensional nonlinear inverse problem because the velocities and thickness of each layer must be inverted simultaneously. Nonlinear methods such as simulated annealing (SA) are commonly used to solve this inverse problem. However, SA controls the iterative process though temperature rather than the error, and the search direction is random; hence, SA always falls into a local optimum when the temperature setting is inaccurate. Specifically, for the inversion of Rayleigh wave dispersion curves, the inversion accuracy will decrease with an increasing number of layers due to the greater number of inversion parameters and large dimension. To solve the above problems, we convert the multiparameter, high-dimensional inverse problem into multiple low-dimensional optimizations to improve the algorithm accuracy by incorporating the principle of block coordinate descent (BCD) into SA. Then, we convert the temperature control conditions in the original SA method into error control conditions. At the same time, we introduce the differential evolution (DE) method to ensure that the iterative error steadily decreases by correcting the iterative error direction in each iteration. Finally, the inversion stability is improved, and the proposed inversion method, the block coordinate descent differential evolution simulated annealing (BCDESA) algorithm, is implemented. The performance of BCDESA is validated by using both synthetic data and field data from western China. The results show that the BCDESA algorithm has stronger global optimization capabilities than SA, and the inversion results have higher stability and accuracy. In addition, synthetic data analysis also shows that BCDESA can avoid the problems of the conventional SA method, which assumes the S-wave velocity structure in advance. The robustness and adaptability of the algorithm are improved, and more accurate shear-wave velocity and thickness information can be extracted from Rayleigh wave dispersion curves.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 35-46"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544121000265/pdfft?md5=de63320a8d985af27d0f2f4d9f31af20&pid=1-s2.0-S2666544121000265-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88055129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arriving at estimates of a rate and state fault friction model parameter using Bayesian inference and Markov chain Monte Carlo","authors":"Saumik Dana ,&nbsp;Karthik Reddy Lyathakula","doi":"10.1016/j.aiig.2022.02.003","DOIUrl":"10.1016/j.aiig.2022.02.003","url":null,"abstract":"<div><p>The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few me-ters depending on the length scale of the critically stressed fault. This makes it incredibly important to construct an inversion framework that provides good estimates of the critical slip distance purely based on the observed ac-celeration at the seismogram. To eventually construct a framework that takes noisy seismogram acceleration data as input and spits out robust estimates of critical slip distance as the output, we first present the performance of the framework for synthetic data. The framework is based on Bayesian inference and Markov chain Monte Carlo methods. The synthetic data is generated by adding noise to the acceleration output of spring-slider-damper idealization of the rate and state model as the forward model.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 171-178"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266654412200003X/pdfft?md5=d2c0b7ab1c68a598ad266f5e72d9539f&pid=1-s2.0-S266654412200003X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80729165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of neural network to speed-up equilibrium calculations in compositional reservoir simulation","authors":"Wagner Q. Barros,&nbsp;Adolfo P. Pires","doi":"10.1016/j.aiig.2022.03.004","DOIUrl":"10.1016/j.aiig.2022.03.004","url":null,"abstract":"<div><p>Compositional reservoir simulation is an important tool to model fluid flow in oil and gas reservoirs. Important investment decisions regarding oil recovery methods are based on simulation results, where hundred or even thousand of different runs are performed. In this work, a new methodology using artificial intelligence to learn the thermodynamic equilibrium is proposed. This algorithm is used to replace the classical equilibrium workflow in reservoir simulation. The new method avoids the stability test for single-phase cells in most cases and provides an accurate two-phase flash initial estimate. The classical and the new workflow are compared for a gas-oil mixing case, showing a simulation time speed-up of approximately 50%. The new method can be used in compositional reservoir simulations.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 202-214"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000119/pdfft?md5=2fd942afd4815b48c4d859e28ca542e8&pid=1-s2.0-S2666544122000119-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78159828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrocarbon detections using multi-attributes based quantum neural networks in a tight sandstone gas reservoir in the Sichuan Basin, China","authors":"Ya-juan Xue ,&nbsp;Xing-jian Wang ,&nbsp;Jun-xing Cao ,&nbsp;Xiao-Fang Liao","doi":"10.1016/j.aiig.2022.02.004","DOIUrl":"10.1016/j.aiig.2022.02.004","url":null,"abstract":"<div><p>A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields. The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data clustering and local wave decomposition based seismic attenuation characteristics, relative wave impedance features of prestack seismic data as the selected multiple attributes for one tight sandstone gas reservoir and further employ principal component analysis combined with quantum neural networks for giving the distinguishing results of the weak responses of the gas reservoir, which is hard to detect by using the conventional technologies. For the seismic data from a tight sandstone gas reservoir in the Sichuan basin, China, we found that multi-attributes based quantum neural networks can effectively capture the weak seismic responses features associated with gas saturation in the gas reservoir. This study is hoped to be useful as an aid for hydrocarbon detections for the gas reservoir with the characteristics of the weak seismic responses by the complement of the multi-attributes based quantum neural networks.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 107-114"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000041/pdfft?md5=76834ca3dea69c31faac8150d745222d&pid=1-s2.0-S2666544122000041-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72547667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven approaches for time series prediction of daily production in the Sulige tight gas field, China","authors":"Qi Zhang ,&nbsp;Ziwei Chen ,&nbsp;Yuan Zeng ,&nbsp;Hang Gao ,&nbsp;Qiansheng Wei ,&nbsp;Tiaoyu Luo ,&nbsp;Zhiguo Wang","doi":"10.1016/j.aiig.2022.02.005","DOIUrl":"10.1016/j.aiig.2022.02.005","url":null,"abstract":"<div><p>The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has exceeded 3,000, keeping the stable gas supply in the decade. Thus, the daily production prediction of gas wells is significant for monitoring production and for implementing and evaluating stimulation measures. Therefore, on the basis of the three data-driven time series approaches, the daily production of 1692 wells over 10 years was mining for the daily production prediction of wells in Sulige. The jointed deep long short-term memory and fully connected neural network (DLSTM-FNN) model was proposed by introducing the recurrent neural network's sequential expression ability and was compared with random forest (RF) and support vector regression (SVR). After the daily production predictions of thousands of wells in Sulige, the proposed DLSTM-FNN model significantly improved the time series prediction accuracy and efficiency in the short training samples and had strong availability and practicability in the Sulige tight gas field.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 165-170"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000053/pdfft?md5=9b3fd0bcc5893aa9caff7afb6886b17a&pid=1-s2.0-S2666544122000053-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82158714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random forest for spatial prediction of censored response variables","authors":"Francky Fouedjio","doi":"10.1016/j.aiig.2022.02.001","DOIUrl":"10.1016/j.aiig.2022.02.001","url":null,"abstract":"<div><p>The spatial prediction of a continuous response variable when spatially exhaustive predictor variables are available within the region under study has become ubiquitous in many geoscience fields. The response variable is often subject to detection limits due to limitations of the measuring instrument or the sampling protocol used. Consequently, the response variable's observations are censored (left-censored, right-censored, or interval-censored). Machine learning methods dedicated to the spatial prediction of uncensored response variables can not explicitly account for the response variable's censored observations. In such cases, they are routinely applied through ad hoc approaches such as ignoring the response variable's censored observations or replacing them with arbitrary values. Therefore, the response variable's spatial prediction may be inaccurate and sensitive to the assumptions and approximations involved in those arbitrary choices. This paper introduces a random forest-based machine learning method for spatially predicting a censored response variable, in which the response variable's censored observations are explicitly taken into account. The basic idea consists of building an ensemble of regression tree predictors by training the classical regression random forest on the subset of data containing only the response variable's uncensored observations. Then, the principal component analysis applied to this ensemble allows translating the response variable's observations (uncensored and censored) into a linear equalities and inequalities system. This system of linear equalities and inequalities is solved through randomized quadratic programming, which allows obtaining an ensemble of reconstructed regression tree predictors that exactly honor the response variable's observations (uncensored and censored). The response variable's spatial prediction is then obtained by averaging this latter ensemble. The effectiveness of the proposed machine learning method is illustrated on simulated data for which ground truth is available and showcased on real-world data, including geochemical data. The results suggest that the proposed machine learning technique allows greater utilization of the response variable's censored observations than ad hoc methods.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 115-127"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000016/pdfft?md5=5c1b45229424d5b90fff743abbbc97b8&pid=1-s2.0-S2666544122000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89568532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}