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Results
Complex spherical-wave seismic inversion in anelastic media for the P-wave minimum quality factor
Cheng, Guangsen (Qilu University of Technology (Shandong Academy of Sciences)) | He, Chuanlin (Qilu University of Technology (Shandong Academy of Sciences)) | Zong, Zhaoyun (China University of Petroleum (East China)) | Liang, Zhanyuan (Qilu University of Technology (Shandong Academy of Sciences)) | Yin, Xingyao (China University of Petroleum (East China)) | Zhang, Xiaoyu (Qilu University of Technology (Shandong Academy of Sciences))
ABSTRACT Attenuation always exists when seismic waves propagate in underground anelastic media, especially in hydrocarbon-bearing reservoirs. Quality factor Q or attenuation factor 1/Q can be used to quantify the seismic wave attenuation and has become an important hydrocarbon indicator. The relationship between the plane-wave reflection coefficient () in anelastic media and P- and S-wave quality factors has been widely used in the plane-wave seismic inversion to estimate the quality factors. The provides an adequate approximation for the deeper subsurface. However, for the shallow subsurface and anelastic wavefields excited by point sources, the is inaccurate and its meaning involves some fundamental difficulties. In view of this, a Q-dependent P-P spherical-wave reflection coefficient () in anelastic media is used here. Considering that having too many parameters to be inverted will lead to unstable and inaccurate inversion results, we further derive an approximate anelastic and anelastic spherical-wave impedance (), which are frequency dependent and are the functions of P- and S-wave velocities, density, and P-wave minimum quality factor (). Finally, a complex spherical-wave seismic inversion approach in anelastic media for the P-wave minimum quality factor is developed. Using the Bayesian inversion approach and complex convolution model, we first estimate the multilayer from the complex seismic traces with different frequencies and incidence angles. Based on the inverted angle- and frequency-dependent , the P- and S-wave velocities, density, and P-wave minimum quality factor are further estimated using a nonlinear inversion tool. Synthetic examples verify the feasibility and robustness of the complex spherical-wave seismic inversion approach in anelastic media. In the shallow subsurface, the spherical-wave inversion is superior to plane-wave inversion. A field example further demonstrates the accuracy and great potential of our approach in hydrocarbon-bearing reservoir prediction.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
ABSTRACT The S-wave velocity () is a vital parameter for various petrophysical, geophysical, and geomechanical applications in subsurface characterization. Nevertheless, obtaining shear sonic log is frequently challenging because of its high economic, time, and operating costs. Conventional methods for predicting rely on empirical relationships and rock-physics models, which often fall short in accuracy due to their inability to account for the complex factors influencing the relationship between and other parameters. We develop a physics-guided machine learning (ML) approach to predict the shear sonic log using various physical parameters (e.g., natural gamma ray, P-wave velocity, density, and resistivity) that can be readily obtained from standard logging suites. Three types of rock-physical constraints combined with three guidance strategies form the various physics-guided models. Specifically, the three constraint models include mudrock line, empirical P- and S-wave velocity relationship, and multiparameter regression from the logging data, and the three guidance strategies involve physics-guided pseudolabels, physics-guided loss function, and transfer learning. To assess the model’s generalization ability and simulate the lack of labeled data in real-world applications, a single well is used as a training well, whereas the remaining four wells are used to blind test in a clastic reservoir. Compared with supervised ML without any constraints, all models incorporating physical constraints demonstrate a significant improvement in prediction accuracy and generalization performance. This underscores the importance of integrating the first-order physical laws into the network training for shear sonic log prediction. The most successful approach combines the multiparameter regression relationship with the physics-guided pseudolabels in this case, resulting in a remarkable 47% reduction in the average root-mean-square error during the blind test.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.36)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.46)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (21 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
A method for transforming aliased modes to true modes based on density clustering and Riemann sheets selection in acoustic logging dispersion inversion
Zhang, Chao (Harbin Institute of Technology) | Chen, Da (Harbin Institute of Technology) | Hu, Hengshan (Harbin Institute of Technology) | Wang, Jun (Harbin Institute of Technology) | He, Xiaodong (Harbin Institute of Technology, Shenzhen STRONG Advanced Materials Research Institute Co., Ltd)
ABSTRACT Dispersion curves obtained from well logs provide crucial formation information around the borehole. An essential step in the inversion of such curves is to eliminate aliased modes, which can potentially interfere with true modes at higher frequencies. The traditional approach involves manually matching the inversion slowness values with the corresponding wave modes at each frequency, which is time consuming and labor intensive. In addition, the traditional method is applicable for the elimination of the aliased modes that are far from true modes, but it is ineffective in resolving aliased modes that approach or even intersect with the true modes. To address these limitations, a novel method is developed to transform aliased modes into true modes. The proposed method involves two key steps. First, the slownesses of individual modes are automatically picked by the density clustering algorithm. Second, based on the Riemann sheets selection of slowness, aliased modes are transformed into true modes by using the single valuedness of the wave-mode spectra and the correspondence between the amplitude spectrum and the slowness. In addition, a theoretical background is provided by introducing the concept of Riemann sheets to explain the origin of aliased modes in the computation. The proposed method can obtain comprehensive and precise dispersion curves, even in cases for which the true modes are interfered with or crossed by aliased modes. The accuracy of the method is validated by comparing the inversion results with the forward model’s dispersion curves. Furthermore, the robustness of the approach is determined by applying it to synthetic waveforms with noise and field waveforms.
- Research Report > New Finding (0.46)
- Research Report > Experimental Study (0.46)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.93)
- Information Technology > Data Science (0.88)
ABSTRACT Hydrocarbon prospect risk assessment is an important process in oil and gas exploration involving the integrated analysis of various geophysical data modalities, including seismic data, well logs, and geologic information, to estimate the likelihood of drilling success for a given drill location. Over the years, geophysicists have attempted to understand the various factors at play influencing the probability of success for hydrocarbon prospects. Toward this end, a large database of prospect drill outcomes and associated attributes has been collected and analyzed via correlation-based techniques to determine the features that contribute the most in deciding the final outcome. Machine learning (ML) has the potential to model complex feature interactions to learn input-output mappings for complicated high-dimensional data sets. However, in many instances, ML models are not interpretable to end users, limiting their utility toward understanding the underlying scientific principles for the problem domain and being deployed to assist in the risk assessment process. In this context, we leverage the concept of explainable ML to interpret various black-box ML models trained on the aforementioned prospect database for risk assessment. Using various case studies on real data, we determine that this model-agnostic explainability analysis for prospect risking can (1) reveal novel scientific insights into the interplay of various features in regard to deciding prospect outcome, (2) assist with performing feature engineering for ML models, (3) detect bias in data sets involving spurious correlations, and (4) build a global picture of a model’s understanding of the data by aggregating local explanations on individual data points.
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Management > Risk Management and Decision-Making (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
Feature-based magnetotelluric inversion by variational autoencoder using a subdomain encoding scheme
Zhou, Hongyu (Tsinghua University) | Guo, Rui (Tsinghua University) | Li, Maokun (Tsinghua University) | Yang, Fan (Tsinghua University) | Xu, Shenheng (Tsinghua University) | Abubakar, Aria (Schlumberger)
ABSTRACT Magnetotelluric (MT) data inversion aims to reconstruct a subsurface resistivity model that minimizes the discrepancy between inverted and measured electromagnetic data. Conventional pixel-based minimum-structure inversion often yields a smoothed-out reconstruction with a relatively low resolution. A priori geophysical knowledge can be embedded into inversion and improve the reconstruction resolution through proper reparameterization. However, existing reparameterization approaches, such as model-based and parametric transform-based inversion, have limited ability to incorporate various a priori information. The effectiveness of existing deep generative model-based inversion algorithms is still debatable when applied to scenarios with complex backgrounds. We develop a feature-based MT data inversion method based on a variational autoencoder (VAE) with a subdomain encoding scheme. Instead of encoding the entire domain of an investigation, we adopt a 1D subdomain encoding scheme to encode the 1D resistivity-depth models using a single VAE. The latent variables for the 2D model are a combination of the latent variables for 1D models, and the encoded region of interest (ROI) can be flexibly determined. The latent variables of ROI and the pixels outside the ROI are simultaneously inverted using the gradient-descent method. Our 1D subdomain encoding scheme reduces the complexity and diversity of the data set, and it can flexibly embed a priori knowledge with various uncertainties. Synthetic data inversion and inversion of the Southern African Magnetotelluric Experiment field data validate our method’s ability to effectively improve inversion accuracy and resolution.
- North America > United States (0.67)
- Africa (0.46)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Cross-well tomography (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
ABSTRACT Machine-learning (ML) applications in seismic exploration are growing faster than applications in other industry fields, mainly due to the large amount of acquired data for the exploration industry. The ML algorithms are constantly being implemented for almost all the steps involved in seismic processing and interpretation workflow, mainly for automation, processing time reduction, efficiency, and in some cases for improving the results. We carry out a literature-based analysis of existing ML-based seismic processing and interpretation published in SEG and EAGE literature repositories and derive a detailed overview of the main ML thrusts in different seismic applications. For each publication, we extract various metadata about ML implementations and performances. The data indicate that current ML implementations in seismic exploration are focused on individual tasks rather than a disruptive change in processing and interpretation workflows. The metadata indicate that the main targets of ML applications for seismic processing are denoising, velocity model building, and first-break picking, whereas, for seismic interpretation, they are fault detection, lithofacies classification, and geobody identification. Through the metadata available in publications, we obtain indices related to computational power efficiency, data preparation simplicity, real data test rate of the ML model, diversity of ML methods, etc., and we use them to approximate the level of efficiency, effectivity, and applicability of the current ML-based seismic processing and interpretation tasks. The indices of ML-based processing tasks indicate that current ML-based denoising and frequency extrapolation have higher efficiency, whereas ML-based quality control is more effective and applicable compared with other processing tasks. Among the interpretation tasks, ML-based impedance inversion indicates high efficiency, whereas high effectivity is depicted for fault detection. ML-based lithofacies classification, stratigraphic sequence identification, and petro/rock properties inversion exhibit high applicability among other interpretation tasks.
- North America > United States (1.00)
- Europe (1.00)
- Geology > Rock Type > Sedimentary Rock (0.68)
- Geology > Geological Subdiscipline > Geomechanics (0.66)
- Geology > Geological Subdiscipline > Stratigraphy (0.48)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying > Microseismic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (0.92)
- Information Technology > Data Science > Data Mining (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- (2 more...)
Seismic attribute analysis with a combination of convolutional autoencoder and random forest in a turbidite reservoir
Wang, Qiannan (Xi’an Jiaotong University) | Wang, Zhiguo (Xi’an Jiaotong University) | Gao, Dengliang (West Virginia University) | Gao, Zhaoqi (Xi’an Jiaotong University) | Jia, Junxiong (Xi’an Jiaotong University) | Zhu, Jianbing (Sinopec) | Gao, Jinghuai (Xi’an Jiaotong University)
ABSTRACT Due to the complex depositional environment of a turbidite reservoir in the Niuzhuang Delta, China, the traditional seismic facies classification is a challenge to perform accurately and continuously. Due to the thin turbidite layers in the reservoir, machine-learning-based prediction of sandstone thickness is challenging. Inspired by the autoencoder, we develop an open-source deep-learning workflow that combines unsupervised and supervised learning with jointed latent eigenvalues of the convolutional autoencoder (CAE) and traditional seismic attributes for seismic facies classification and sandstone thickness prediction constrained by the facies distribution. First, we extract lower-dimensional latent eigenvalues as a category of novel seismic attributes from the seismic data using a CAE. To accurately and effectively extract lower-dimensional latent eigenvalues, we develop a hybrid loss function based on the mean-squared error loss and the smooth L1 loss in this CAE. Then, we use principal component (PC) analysis to extract the first four PCs of these seismic lower-dimensional latent eigenvalues. Using unsupervised K-means, we cluster the first four PCs to form seismic facies. Finally, we take the first four PCs with the traditional seismic attributes as input and the sandstone thickness as labels for the random forest to predict the sandstone thickness distribution. The results of seismic facies and sandstone thickness distribution confirm the potential and advantages of our workflow, which can speed up the identification of seismic facies with smoother boundaries, improve the prediction accuracy by 16% over than that of traditional seismic attributes, and provide more depositional insight for a turbidite reservoir of the Shahejie Formation in the Niuzhuang Delta, China.
- North America > United States (0.93)
- Asia > China > Shandong Province (0.69)
- Geology > Sedimentary Geology > Depositional Environment > Marine Environment > Deep Water Marine Environment (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (1.00)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- Europe > Russia > Barents Sea > East Barents Sea Basin > Shtokmanovskoye Field (0.99)
- Asia > Pakistan > Sindh > Khairpur District > Miano Field (0.99)
- (9 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (0.46)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.34)
Multiple-frequency attribute blending via adaptive uniform manifold approximation and projection and its application on hydrocarbon reservoir delineation
Liu, Naihao (Xi’an Jiaotong University) | Zhang, Zezhou (Xi’an Jiaotong University) | Zhang, Haoran (Xi’an Jiaotong University) | Wang, Zhiguo (Xi’an Jiaotong University) | Gao, Jinghuai (Xi’an Jiaotong University) | Liu, Rongchang (PetroChina Research Institute of Petroleum Exploration and Development (RIPED)) | Zhang, Nan (Yumen Oilfield Company)
ABSTRACT Multifrequency attribute blending is a highly effective tool for characterizing hydrocarbon reservoirs. It begins by extracting multifrequency attributes of seismic data based on time-frequency transformation. Subsequently, a blending algorithm is used to fuse the extracted multifrequency components, thereby obtaining the interpretation results of the interested reservoirs. The red-green-blue (RGB) algorithm is commonly used to fuse the multifrequency components. However, it should be noted that the RGB blending algorithm can only fuse three frequency components, i.e., the low-, middle-, and high-frequency components. Moreover, it can occasionally introduce ambiguities, making it difficult to interpret areas that appear white or yellow. To address these issues, we develop a workflow for multiple-frequency component analysis to delineate hydrocarbon reservoirs. First, we apply the generalized S-transform to obtain the multiple-frequency components of seismic data. Then, the correlation analysis is developed and implemented to select the sensitive frequency components. Finally, we use the uniform manifold approximation and projection, a nonlinear dimension reduction algorithm, to blend the extracted multiple-frequency components and obtain reservoir interpretation results. We apply the suggested workflow to synthetic data and a 3D field data volume to evaluate its effectiveness. Our mathematical analysis demonstrates that the suggested workflow can effectively fuse multiple-frequency components to accurately characterize hydrocarbon reservoirs.
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.46)
- Oceania > New Zealand > South Island > South Pacific Ocean > Canterbury Basin (0.99)
- Asia > China > Shanxi > Ordos Basin (0.99)
- Asia > China > Shaanxi > Ordos Basin (0.99)
- (5 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Data Science (0.93)
A method for transforming aliased modes to true modes based on density clustering and Riemann sheets selection in acoustic logging dispersion inversion
Zhang, Chao (Harbin Institute of Technology) | Chen, Da (Harbin Institute of Technology) | Hu, Hengshan (Harbin Institute of Technology) | Wang, Jun (Harbin Institute of Technology) | He, Xiaodong (Harbin Institute of Technology, Shenzhen STRONG Advanced Materials Research Institute Co., Ltd)
ABSTRACT Dispersion curves obtained from well logs provide crucial formation information around the borehole. An essential step in the inversion of such curves is to eliminate aliased modes, which can potentially interfere with true modes at higher frequencies. The traditional approach involves manually matching the inversion slowness values with the corresponding wave modes at each frequency, which is time consuming and labor intensive. In addition, the traditional method is applicable for the elimination of the aliased modes that are far from true modes, but it is ineffective in resolving aliased modes that approach or even intersect with the true modes. To address these limitations, a novel method is developed to transform aliased modes into true modes. The proposed method involves two key steps. First, the slownesses of individual modes are automatically picked by the density clustering algorithm. Second, based on the Riemann sheets selection of slowness, aliased modes are transformed into true modes by using the single valuedness of the wave-mode spectra and the correspondence between the amplitude spectrum and the slowness. In addition, a theoretical background is provided by introducing the concept of Riemann sheets to explain the origin of aliased modes in the computation. The proposed method can obtain comprehensive and precise dispersion curves, even in cases for which the true modes are interfered with or crossed by aliased modes. The accuracy of the method is validated by comparing the inversion results with the forward model’s dispersion curves. Furthermore, the robustness of the approach is determined by applying it to synthetic waveforms with noise and field waveforms.
- Research Report > New Finding (0.46)
- Research Report > Experimental Study (0.46)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.93)
- Information Technology > Data Science (0.88)
Multiple-frequency attribute blending via adaptive uniform manifold approximation and projection and its application on hydrocarbon reservoir delineation
Liu, Naihao (Xi’an Jiaotong University) | Zhang, Zezhou (Xi’an Jiaotong University) | Zhang, Haoran (Xi’an Jiaotong University) | Wang, Zhiguo (Xi’an Jiaotong University) | Gao, Jinghuai (Xi’an Jiaotong University) | Liu, Rongchang (PetroChina Research Institute of Petroleum Exploration and Development (RIPED)) | Zhang, Nan (Yumen Oilfield Company)
ABSTRACT Multifrequency attribute blending is a highly effective tool for characterizing hydrocarbon reservoirs. It begins by extracting multifrequency attributes of seismic data based on time-frequency transformation. Subsequently, a blending algorithm is used to fuse the extracted multifrequency components, thereby obtaining the interpretation results of the interested reservoirs. The red-green-blue (RGB) algorithm is commonly used to fuse the multifrequency components. However, it should be noted that the RGB blending algorithm can only fuse three frequency components, i.e., the low-, middle-, and high-frequency components. Moreover, it can occasionally introduce ambiguities, making it difficult to interpret areas that appear white or yellow. To address these issues, we develop a workflow for multiple-frequency component analysis to delineate hydrocarbon reservoirs. First, we apply the generalized S-transform to obtain the multiple-frequency components of seismic data. Then, the correlation analysis is developed and implemented to select the sensitive frequency components. Finally, we use the uniform manifold approximation and projection, a nonlinear dimension reduction algorithm, to blend the extracted multiple-frequency components and obtain reservoir interpretation results. We apply the suggested workflow to synthetic data and a 3D field data volume to evaluate its effectiveness. Our mathematical analysis demonstrates that the suggested workflow can effectively fuse multiple-frequency components to accurately characterize hydrocarbon reservoirs.
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.46)
- Oceania > New Zealand > South Island > South Pacific Ocean > Canterbury Basin (0.99)
- Asia > China > Shanxi > Ordos Basin (0.99)
- Asia > China > Shaanxi > Ordos Basin (0.99)
- (5 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Data Science (0.93)