PETROPHYSICS 包含对地层评价的理论和应用方面的原创性贡献，包括裸眼和套管井测井、岩心分析和地层测试。

The journal "Petrophysics" publishes original research contributions on the theory and application of formation evaluation, including open-hole and cased-hole logging, core analysis, and formation testing.

《Petrophysics》期刊发表关于地层评价的原创性研究，包括裸眼和套管井测井、岩心分析及地层测试等理论与应用方面的贡献。

Petrophysics

Accurate prediction of CH4 solubility in oil-based mud is significant for evaluating gas kick severity and implementing proper well control. The main factors affecting gas solubility in oil-based drilling fluid are base oil content, pressure, temperature, drilling fluid viscosity, and the components in natural gas. This work tests the complete series of solubility of high-purity CH4 in oil-based mud simulating downhole conditions on a 5,000-m-deep well relative to temperature, pressure, viscosity, and base oil content with temperature ranging from 40°C to 140°C and pressure ranging from 2 to 56 MPa using a PVT autoclave. Then, the effects of temperature, pressure, base oil content, and drilling fluid viscosity on the test target are analyzed using multiple experimental data processing methods. It is found that the variation of methane solubility with pressure and temperature are almost linearly under interested ranges. The effects of pressure (0.205 to ~0.294%/MPa) are much higher than that of temperature (–0.008 to –0.011%/°C) in oil-based drilling mud; the solubility of methane decreases as viscosity increases. Models for predicting methane solubility have been developed using screening procedures and statistics regression, which can be integrated into gas-liquid flow models to investigate flow behavior while gas kicking.

Xuncheng Song

Bernard Francis Sukari

Lei Wang

Zhiqiang Jiang

Jun Cai

Yuqiang Xu

Hongqiang Huang

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description

Experimental Investigation on the Effect of Methane Solubility in Oil-Based Mud Under Downhole Conditions

井下条件下甲烷在油基泥浆中溶解度的实验研究

Complex lithology petrophysical interpretation with multiphysics logging tools has been and continues to be a major challenge in formation evaluation. Many currently used data-driven approaches, such as a neural network (NN), deliver predicted results in numerical quantities rather than analytical equations. It is more challenging if multiphysics logging measurements are collectively used to estimate a petrophysical parameter. To overcome these problems, a physics-guided, artificial intelligence (AI) machine-learning (ML) method for petrophysical interpretation model development is described. The workflow consists of the following five constituents: (1) statistical tools such as correlation heatmaps are employed to select the best candidate input variables for the target petrophysical equations; (2) a genetic programming-based symbolic regression approach is used to fuse multiphysics measurements data for training the petrophysical prediction equations; (3) an optional ensemble modeling procedure is applied for maximally utilizing all available training data by integrating multiple instances of prediction equations objectively, which is especially useful for a small training data set; (4) a means of obtaining conditional branching in prediction equations is enabled in symbolic regression to handle certain formation heterogeneity; and (5) a model discrimination framework is introduced to finalize the model selection based on mathematical complexity, physics complexity, and model performance. The efficacy of the five-constituents petrophysical interpretation development process is demonstrated on a data set collected from six wells with the goal of obtaining formation resistivity factor (F) and permeability (k) equations for heterogeneous carbonate reservoirs. We show quantitatively how individual constituents of the workflow improve the model performance with two error metrics. A comparison of NN-method-predicted permeability values vs. SR-based-workflow-predicted permeability equation is included to showcase many advantages of the latter. Beyond the transparency of an analytical form of the prediction equations, the SR method intrinsically has a more relaxed requirement on the training data size, is less prone to overfitting, yet can deliver superior model performance rival to the NN approach.

Songhua Chen

Wei Shao

Huiwen Sheng

Hyung Kwak

Use of Symbolic Regression for Developing Petrophysical Interpretation Models

利用符号回归开发测井解释模型

Data analytics facilitate the examination of spatial data sets by using multiple techniques to find and understand patterns to guide decision making. However, standard data analysis tools assume that the data are independent and identically distributed, an assumption that spatial data sets usually do not fulfill. Furthermore, the usual methods neglect spatial continuity and the inherent data paucity that should be considered in the data analytics workflow. We present a new approach that combines data analytics, geostatistics, and optimization techniques to provide an end-to-end workflow to analyze two-dimensional (2D) data sets. The proposed workflow identifies outliers based on their spatial location or distribution, models geological trends using a Gaussian kernel, models the semivariogram, and performs sequential Gaussian simulation applying kriging or cokriging for cosimulation. Moreover, it provides metrics and diagnostic plots to evaluate the goodness of the results at each step. It is also semiautomatic because it leverages the user’s judgment for subsequent operations. For optimization, the workflow uses Bayesian optimization and evolutionary algorithms. We demonstrate the use of the workflow by analyzing 1,152 wells over the Duvernay Formation in Canada. The examples include the simulation of density-porosity as the secondary feature and the cosimulation of total organic content constrained by the former. The proposed workflow helps focus more on interpreting the results than the modeling parameters, reducing workforce time and subjective errors. Moreover, the spatial simulation includes multiple realizations to assess uncertainty and support decision making in data paucity scenarios. Overall, the proposed workflow is a valuable and complementary tool for evaluating uncertainty in mature geospatial data.

Jose J. Salazar

Jesus Ochoa

Léan Garland

Larry W. Lake

Michael J. Pyrcz

Spatial Data Analytics-Assisted Subsurface Modeling: A Duvernay Case Study

空间数据分析辅助的地下建模：Duvernay案例研究

Sonic well logs provide critical information to calibrate seismic data and support geomechanical characterization. Advanced subsurface data analytics and machine learning enable new methods and workflows for property estimation, regression, and classification for geoscience and subsurface engineering applications. However, current applications for imputation of well-logging values rely only on model accuracy and low error predictions. T raditional model validation techniques are not enough to validate models and account for the substantial uncertainty in the subsurface. Well-logging imputation estimates and their associated uncertainty models are essential to the field development planning and decision-making workflows, such as reservoir modeling, volumetric resource assessment, predrill prediction with uncertainty, remaining resource mapping, and production allocation. When performing subsurface feature imputation with machine learning, we must expand our machine-learning model training and complexity tuning workflows to check the entire uncertainty model to ensure uncertainty distributions are precise and accurate. We propose a workflow that integrates the goodness metric to calculate accurate and precise uncertainty models of sonic well-log predictions based on ensembles of the machine-learning estimates. Our workflow combines model evaluation and visualization of the estimates and the uncertainty model with respect to measured depth. Our proposed method provides intuitive diagnostics and metrics to evaluate estimation accuracy and uncertainty model goodness.

Eduardo Maldonado-Cruz

John T. Foster

Sonic Well-Log Imputation Through Machine-Learning-Based Uncertainty Models

基于机器学习的不确定性模型的声波测井数据插补

In this paper, a supervised machine-learning (ML) method to remove artifacts and noise from borehole images is described. Borehole images may exhibit a variety of issues and artifacts due to reasons such as environmental and thermal noise, imperfect calibration, and current leakage through the tool body. Methods that are currently employed to improve these images are based on traditional signal-processing techniques. Although these methods are capable of removing the artifacts in images and significantly improving image quality, they have some drawbacks as well. These drawbacks include not being entirely suitable for real-time implementation and issues with reproducibility. The alternative method presented here is based on an ML algorithm that is trained using a data set pairing raw data with data processed using a traditional signal-processing-based approach. The resulting ML model is capable of being implemented in near-real time. Furthermore, the application of the algorithm does not require user supervision, increasing the reproducibility of the results.

Baris Guner

Ahmed E. Fouda

Peter Barrett

Removal of Artifacts in Borehole Images Using Machine Learning

使用机器学习去除井眼图像中的伪影

Permeability is an important characteristic for reservoir characterization and fluid flow modeling. Besides the more conventional sampling by core plugs and whole cores for measurements, the probe permeameter (sometimes called a mini-permeameter) has provided an alternative, nondestructive method to assess permeability. Application of the probe permeameter, however, may require some care before useful results can be obtained. In this study, we describe the testing and modifications of a commercially available probe permeameter. We identify some limitations and propose two modifications. With the modified device, a core from the Lower Wilcox Formation, onshore Texas, USA, is evaluated for the purposes of assessing CO2 injectivity and trapping in the formation. We find that systematic testing of the probe improved understanding of its response and offers a guide for evaluating any probe permeameter. Problems reported in a prior published study can be remedied, making the probe a reliable device that can measure permeabilities in the range of 0.023 to 760 md. We found problems caused by surface impairment, likely due to the long-term storage of the studied core, which reduced probe permeabilities in the more permeable intervals. We devised an adjustment to the readings to address this issue. We find grain size to be a major control of permeability in the non-marine section of the core (e.g., the fluvial-dominated distributary channels), but grain size has a weaker effect on the marine intervals (e.g., the wave-dominated delta front). CO2 injectivity is predicted to be best in the non-marine intervals (i.e., fluvial facies), but trapping is likely to be modest. Better trapping but reduced injectivity is expected to occur in the marine intervals.

Jerry L. Jensen

Carlos A. Uroza

Testing and Application of a Probe Permeameter in the Lower Wilcox Formation, Onshore Texas, USA

德克萨斯州近海威尔科克斯组下段探针渗透率仪的测试与应用

Logging-while-drilling (LWD) nuclear magnetic resonance (NMR) tools play a crucial role in reservoir evaluation. However, in horizontal wells with thin layers smaller than the tool’s sensitive region, deciphering the contribution of each layer to the total signal poses a formidable challenge. The general thinking is that all the thin layers are contributing the same to the total acquired signal. However, the reality is more complex than just a box car response function. This complexity arises from the layer’s position relative to the cylindrical sensitive region, making it challenging to develop accurate forward models for LWD NMR tools in such scenarios. A theoretical response scheme was constructed to try to explain how the LWD NMR tools respond in such scenarios. However, conducting an experiment to validate the theory was a must to develop algorithms that can be used in forward modeling such complex response cases. To address this challenge, we designed and executed an experiment within a controlled environment—a water tank—that replicates the intricate conditions found in horizontal wells with thin beds. To overcome the practical limitations of measuring specific thin-bed characteristics at various positions in a laboratory, we used the calibration setup of the Slim-LWD NMR tool for measurement purposes. The experiment involved a systematic reduction of the water level in the calibration tank in discrete increments, emulating the vertical movement of a thin bed within the horizontal cylindrical sensitive region of the tool. Data acquisition was conducted at predefined intervals as the water level progressively decreased until the tank was empty. The noise level during this experiment is the standard 6 p.u. per phase alternated pair (PAP), and the final stacking level used is five levels. As the tool is stationary in this tank, no motion effects (axial or lateral) are encountered during the experiment. Through meticulous processing of the acquired water level data, we assessed the individual contribution of each level drop to the original signal. The culmination of this effort yielded a comprehensive lookup table containing the coefficients for horizontal layers as thin as 0.25 in. We believe that the outcome of this experiment can help log analysts have a better understanding of the LWD-NMR tool response in horizontal wells when more than one layer is measured at a single depth. As the experiment has validated the theoretical mathematical response we anticipated, a much thinner layer model can now be safely constructed with any layer thickness.

Zeyad Ramadan

David Allen

Ahmed Allam

Characterizing Thin-Bed Responses in Horizontal Wells Using LWD NMR Tools: Insights From a Water Tank Experiment

利用随钻核磁共振工具表征水平井薄层响应：水箱实验的启示

Lithofacies identification is essential for understanding sedimentary basins, especially in the Cretaceous Lumshiwal Formation of the Kohat Sub-basin, where data scarcity and complex geological structures pose significant challenges. The Kohat Sub-basin, which is part of the tectonically active western Kohat Fold and Thrust Belt, exhibits steep folds and faulting. Therefore, integrated geological and geophysical approaches are essential for effective reservoir characterization. In this study, machine-learning (ML) algorithms are used to enhance the resolution of a sparse data set obtained within a complex geophysical environment. As a result, high-resolution techniques are applied to effectively tackle the challenges faced in highly complex regimes. This study utilizes advanced ML algorithms, specifically the Extra Tree (ET) and Extreme Gradient Boosting (XGB) classifiers, to improve lithofacies prediction within the Kohat Sub-basin. Utilizing well-log data from six wells, including parameters such as gamma ray (GR), neutron porosity (NPHI), resistivity, caliper (CALI), density (RHOB), spontaneous potential, and microspherically focused log (MSFL), the ML models were trained for accurate lithofacies prediction. Red dots denote values outside of the interquartile range (IQR), while heat maps and box plots were utilized to visualize the data and find outliers. The Lumshiwal Formation’s facies were divided into three categories: gas sand, wet sand, and shale, using cutoff values for the Vshale and Sw logs for each well. The agreement between measured and projected lithofacies is shown by confusion matrices. The precision with which lithofacies might be predicted by ML methods was proven through numerical experiments. With an accuracy of almost 94%, the ET classifier beat the XGB classifier. Particularly in heterogeneous reservoirs with different lithofacies, the ET classifier proved to be highly reliable, with lithofacies prediction accuracy ranging from 93 to 95%. The findings demonstrate how advanced ML techniques can enhance reservoir characterization in brownfields with little data, such as the Kohat Sub-basin. This provides insightful information for enhancing exploration tactics and efficient reservoir management.

Hina Wajid Satti

Tahseenullah Khan

Muhammad Fahad Mahmood

Umar Manzoor

Muyyassar Hussain

Muhammad Bilal Malik

Enhancing Reservoir Characterization With High-Resolution Lithofacies Prediction Using Advanced Feature Engineering and Ensemble Classifiers

利用高级特征工程和集成分类器进行高分辨率岩相预测以增强储层表征

Selecting the optimal water saturation (Sw) equation for reservoir characterization can be an intricate task. Numerous equations exist, each with inherent strengths and limitations. The Tambaredjo Field in Suriname has historically employed the Indonesian equation. While recent research suggests the Modified Indonesian equation, originally developed for Athabasca tar sands, offers a better fit for initial water production in multiple wells, its applicability to the Tambaredjo Field’s unique characteristics (unconsolidated and heavy oil) raises concerns. This study proposes a comprehensive methodology to assess Sw equations and identify the most suitable candidate for the Tambaredjo Field. Two crucial steps were undertaken: 1. Comprehensive core data inventory: All core data relevant to Sw equation calibration were compiled, including direct and indirect measurements of irreducible water saturation (Swir). Twenty-seven (27) samples underwent various tests like capillary pressure, mercury injection, resistivity index, nuclear magnetic resonance (NMR), and relative permeability, serving as calibration references for diverse Sw equations (e.g., Indonesian, Dual Water, etc.). 2. Development of the Water Saturation Equation Assessment Tool (WSEAT): This tool facilitates the evaluation and selection of the best-fit equation. WSEAT directly compares individual equation outputs against core sample data while analyzing all sensitive input variations. Evaluation via WSEAT revealed the Modified Indonesian equation as the best initial fit. However, its origin in tar sand applications necessitated further investigation. Modifications, assumption reviews, and solutions for challenging input estimations were explored. Consequently, a novel approach was developed to integrate clay and silt corrections into Sw calculations. Instead of a single term for shale, clay and silt were treated separately. The derivation included formation water resistivity (Rw) for each component (sand, clay, and silt). Additionally, a laminar approach was considered, as this approach has shown better results in some areas of the field. The proposed equations were evaluated well by well to verify their alignment with field production performance. Results in the Tambaredjo Central Area (TAMC) demonstrate a significant improvement in Sw, from 41.4 to 24.5%. This translates to an actual recovery value of 11.2%, which aligns more closely with the anticipated primary recovery for the field. This study presents a comprehensive approach for selecting the optimal Sw equation for unconsolidated reservoirs and exemplifies its successful application in the Tambaredjo Field. The proposed Suriname Clay Sw equation, tailored to the specific reservoir characteristics, offers enhanced accuracy and aligns better with production data.

Elias R. Acosta

Aima Mijland

Bhagwanpersad Nandlal

Unveiling the Optimal Water Saturation Equation for Unconsolidated Reservoirs: A Case Study From the Tambaredjo Oil Field, Suriname

揭示非固结储层最佳含水饱和度方程：苏里南坦巴雷乔油田案例研究

Sealed coring technology has been widely used in the Bohai Bay Basin in the past decades. The temperature and pressure changes experienced by the core from in-place borehole to lab conditions generate inconsistencies between the measured and the in-place saturations. In the Bohai Bay Basin, the as-received air-filled saturation measured in a laboratory has reached 10 ~ 30%, leading to significant uncertainty when using measured saturation to calibrate log-derived saturation. Saturation correction must be performed when attempting to assess the risks, economics, and potential of a targeted resource play. It is commonly assumed that the void space at surface was previously occupied by hydrocarbons. However, this statement clearly overestimates the content of hydrocarbons, especially in waterflood intervals where there is a simultaneous loss of oil and water. The influence of montmorillonite dehydration on Dean-Stark saturation has not been extensively investigated. Although physical experiments can simulate the effects of degasification on fluid saturations, they are not practical for vendors and operators due to their lengthy duration. Too much attention has been focused on updating coring technology and saturation measurement methods, while the key question has been neglected: How has the core saturation changed due to the temperature and pressure changes? The interlayer water of montmorillonite bleeds from the core at high temperatures. The volume of pores and fluids changes as the core is brought to the surface. Some of the fluids in the pore space get volatile because of degasification. All these phenomena can lead to errors in laboratory saturation. This manuscript presents a workflow that includes correction models for pore volume expansion, clay dehydration, and degasification to bridge the gap between measured and in-situ saturations. The corrected saturation shows a good agreement with log-derived and air-mercury injection saturations, indicating the effectiveness of the workflow. Compared with the measured water saturation from standard practice, the corrected water saturation increased by an average of 7.4 saturation units (s.u.) and was found to be statistically significant (P = 0.0268 &lt; 0.05). The workflow is based on experiments that were conducted on hydrophilic clay-quartz samples, so the application of the workflow is limited by the wettability and lithology of the formation.

Jiansheng Zhang

Sainan Xu

Yunlong Li

Yi Qi

Yang Li

A Method for Reconstructing In-Situ Saturation Based on Dean-Stark Saturation Measured in Laboratory

基于实验室Dean-Stark饱和度测量的原位饱和度重建方法

Abbreviation	Petro S Journ
Journal Impact	0.66
Quartiles(Global)	GEOCHEMISTRY & GEOPHYSICS(Q4)

ISSN	1529-9074, 2641-4112
h-index	42

Publication Information	Publisher: Society of Well Log Analystists Inc.，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2017，Proportion of original research papers: 97.87%，Self Citation Rate:14.30%， Gold OA Rate: 0.00%
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：容易

Petrophysics

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