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Pitawala S, Teal PD. Bayesian NMR petrophysical characterization. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 362:107663. [PMID: 38598989 DOI: 10.1016/j.jmr.2024.107663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/16/2024] [Accepted: 03/19/2024] [Indexed: 04/12/2024]
Abstract
Identification of reservoir rock types is necessary for the exploration and recovery of oil and gas. It involves determining the petrophysical properties of rocks such as porosity and permeability which play a significant role in developing reservoir models, estimating the volumes of oil and gas reserves, and planning production methods. Nuclear magnetic resonance (NMR) technology is a fast and accurate tool for petrophysical rock characterization. The distributions of relaxation times (T2 distributions) offer valuable insights into the distribution of pore sizes in rocks, and these distributions are closely linked to important petrophysical parameters like porosity, permeability, and bound fluid volume (BFV). This work introduces a Bayesian estimation method for analyzing NMR data. The Bayesian approach uses prior knowledge of T2 distributions in the form of the prior mean and covariance. The Bayesian approach combines prior knowledge with observed data to obtain improved estimation. We use the Bayesian estimation method where prior information regarding the rock sample type, for example shale, is available. The estimators were evaluated on decay data simulated from synthesized distributions that replicate the features of experimental T2 distributions of three types of reservoir rocks. We compared the performance of the Bayesian method with two existing methods using porosity, bound fluid volume (BFV) geometric mean (T2LM) and root mean square error (RMSE) of the estimated T2 distribution as evaluation criteria. Additional experiments were carried out using experimental T2 distributions to validate the results. The performance of the Bayesian methods was also tested using mismatched priors. The experimental results illustrate that the Bayesian estimator outperforms other estimators in estimating the T2 distribution. The Bayesian method also outperforms the ILT method in estimating derived petrophysical properties except in cases where the noise level is below 0.1 and the T2 distributions are associated with short relaxation times.
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Affiliation(s)
- S Pitawala
- Victoria University of Wellington, Wellington, New Zealand.
| | - P D Teal
- Victoria University of Wellington, Wellington, New Zealand
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Chen M, Yang J, Tang C, Lu X, Wei Z, Liu Y, Yu P, Li H. Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches. Curr Top Med Chem 2024; 24:222-242. [PMID: 38083894 DOI: 10.2174/0115680266280005231207105900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/02/2023] [Accepted: 11/10/2023] [Indexed: 05/04/2024]
Abstract
Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.
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Affiliation(s)
- Meilun Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Jie Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Chunhua Tang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Xiaoling Lu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Zheng Wei
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Yijie Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Peng Yu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - HuanHuan Li
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
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Luo G, Xiao L, Luo S, Liao G, Shao R. A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 346:107358. [PMID: 36525932 DOI: 10.1016/j.jmr.2022.107358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 12/04/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Nuclear magnetic resonance (NMR) is a powerful tool for formation evaluation in the oil industry to determine parameters, such as pore structure, fluid saturation, and permeability of porous materials, which are critical to reservoir engineering. The inversion of the measured relaxation data is an ill-posed problem and may lead to deviations of inversion results, which may degrade the accuracy of further data analysis and evaluation. This paper proposes a deep learning method for multi-exponential inversion of NMR relaxation data to improve accuracy. Simulated NMR data are first constructed using a priori knowledge based on the signal parameters and Gaussian distribution. These data are then used to train the neural network designed to consider noise characteristics, signal decay characteristics, signal energy variations, and non-negative features of the T2 spectra. With the validation from simulated data, the models introduced by multi-scale convolutional neural network (CNN) and attention mechanism outperform other approaches in terms of denoising and T2 inversion. Finally, NMR measurements of rock cores are used to compare the effectiveness of the attention multi-scale convolutional neural network (ATT-CNN) model in practical applications. The results demonstrate that the proposed method based on deep learning has better performance than the regularization method.
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Affiliation(s)
- Gang Luo
- College of Artificial Intelligence, China University of Petroleum, 102249 Beijing, China
| | - Lizhi Xiao
- College of Artificial Intelligence, China University of Petroleum, 102249 Beijing, China.
| | - Sihui Luo
- College of Petroleum Engineering, China University of Petroleum, 102249 Beijing, China
| | - Guangzhi Liao
- College of Artificial Intelligence, China University of Petroleum, 102249 Beijing, China
| | - Rongbo Shao
- College of Artificial Intelligence, China University of Petroleum, 102249 Beijing, China
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Rozowski M, Palumbo J, Bisen J, Bi C, Bouhrara M, Czaja W, Spencer RG. Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2022; 60:1076-1086. [PMID: 35593385 PMCID: PMC10185331 DOI: 10.1002/mrc.5289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/22/2022] [Accepted: 05/17/2022] [Indexed: 05/17/2023]
Abstract
Many methods have been developed for estimating the parameters of biexponential decay signals, which arise throughout magnetic resonance relaxometry (MRR) and the physical sciences. This is an intrinsically ill-posed problem so that estimates can depend strongly on noise and underlying parameter values. Regularization has proven to be a remarkably efficient procedure for providing more reliable solutions to ill-posed problems, while, more recently, neural networks have been used for parameter estimation. We re-address the problem of parameter estimation in biexponential models by introducing a novel form of neural network regularization which we call input layer regularization (ILR). Here, inputs to the neural network are composed of a biexponential decay signal augmented by signals constructed from parameters obtained from a regularized nonlinear least-squares estimate of the two decay time constants. We find that ILR results in a reduction in the error of time constant estimates on the order of 15%-50% or more, depending on the metric used and signal-to-noise level, with greater improvement seen for the time constant of the more rapidly decaying component. ILR is compatible with existing regularization techniques and should be applicable to a wide range of parameter estimation problems.
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Affiliation(s)
- Michael Rozowski
- Applied Mathematics and Statistics, and Scientific Computation, University of Maryland, College Park, Maryland, USA
- Department of Mathematics, University of Maryland, College Park, Maryland, USA
| | - Jonathan Palumbo
- National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Jay Bisen
- National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Chuan Bi
- National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Mustapha Bouhrara
- National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Wojciech Czaja
- Department of Mathematics, University of Maryland, College Park, Maryland, USA
| | - Richard G. Spencer
- National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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Lu R, Bao C, Chen L, Yu Q, Wu Y, Jiang X, Wu Z, Ni Z, Yi H. A novel inversion method of 2D TD-NMR signals based on realizing unconstrained maximization of objective function. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 337:107168. [PMID: 35202918 DOI: 10.1016/j.jmr.2022.107168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 02/10/2022] [Accepted: 02/13/2022] [Indexed: 06/14/2023]
Abstract
The inversion of time-domain nuclear magnetic resonance (TD-NMR) signals is an ill-posed problem, which presents enormous challenges for the inversion algorithm. We propose a novel inversion method that converts conventional minimum objective function with non-negative constraints into an unconstrained maximization problem in the inversion of TD-NMR signals. Hence, the objective function becomes a differentiable concave function that can be solved more easily. The validity of the proposed method was verified by the uncertainty estimation of NMR inversion spectra with different signal-to-noise ratios (SNR). Through the inversion of simulated 2D D-T2 and T1-T2 signals under different SNR, the proposed method was proved to be less sensitive to noise than the conventional inversion method. We use the proposed method to study the migrations of oil and water in shales, the components change in shale could be identified and quantified according to the 2D T1-T2 inversion spectra. The proposed method was also used to analyze the hydration process of cement. The 2D T1-T2 inversion spectra could distinctly present the component of tiny volume with short relaxation time, and the migration regularity of capillary water, gel water, and bound water could also be found. In conclusion, the proposed method could be a reliable method to invert TD-NMR signals, especially the identification of the 2D NMR signals with a short relaxation time in low SNR.
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Affiliation(s)
- Rongsheng Lu
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China; National Key Laboratory of Bioelectronics, Southeast University, Nanjing 211189, China.
| | - Chong Bao
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Lang Chen
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Qiaoming Yu
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Yuchen Wu
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Xiaowen Jiang
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Zhengxiu Wu
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Zhonghua Ni
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China; National Key Laboratory of Bioelectronics, Southeast University, Nanjing 211189, China
| | - Hong Yi
- Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China; School of Mechanical Engineering, Southeast University, Nanjing 211189, China; National Key Laboratory of Bioelectronics, Southeast University, Nanjing 211189, China.
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Altenhof AR, Jaroszewicz MJ, Frydman L, Schurko R. 3D Relaxation-Assisted Separation of Wideline Solid-State NMR Patterns for Achieving Site Resolution. Phys Chem Chem Phys 2022; 24:22792-22805. [DOI: 10.1039/d2cp00910b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
There are currently no methods for the acquisition of ultra-wideline (UW) solid-state NMR spectra under static conditions that enable reliable separation and resolution of overlapping powder patterns arising from magnetically...
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