Direct prediction of petrophysical and petroelastic reservoir properties from seismic and well-log data using nonlinear machine learning algorithms

2019 ◽  
Vol 38 (12) ◽  
pp. 949-958 ◽  
Author(s):  
I. I. Priezzhev ◽  
P. C. H. Veeken ◽  
S. V. Egorov ◽  
U. Strecker
Keyword(s):  
Machine Learning ◽  
Bulk Density ◽  
Learning Algorithms ◽  
Seismic Inversion ◽  
Machine Learning Algorithms ◽  
Well Log ◽  
Convolution Operators ◽  
Reservoir Properties ◽  
Log Data ◽  
Linear Convolution

An analytical comparison of seismic inversion with several multivariate predictive techniques is made. Statistical data reduction techniques are examined that incorporate various machine learning algorithms, such as linear regression, alternating conditional expectation regression, random forest, and neural network. Seismic and well-log data are combined to estimate petrophysical or petroelastic properties, like bulk density. Currently, spatial distribution and estimation of reservoir properties is leveraged by inverting 3D seismic data calibrated to elastic properties (VP, VS, and bulk density) obtained from well-log data. Most commercial seismic inversions are based on linear convolution, i.e., one-dimensional models that involve a simplified plane-parallel medium. However, in cases that are geophysically more complex, such as fractured and/or fluid-rich layers, the conventional straightforward prediction relationship breaks down. This is because linear convolution operators no longer adequately describe seismic wavefield propagation due to nonlinear energy absorption. Such nonlinearity is also suggested by the seismic nonstationarity phenomenon, expressed by vertical and horizontal changes in the shape of the seismic wavelet (amplitude and frequency variations). The nonlinear predictive operator, extracted by machine learning algorithms, makes it possible in certain cases to estimate petrophysical reservoir properties more accurately and with less influence of interpretational bias.

scholarly journals A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1896
Author(s):  
Timur Merembayev ◽  
Darkhan Kurmangaliyev ◽  
Bakhbergen Bekbauov ◽  
Yerlan Amanbek
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil And Gas ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Well Log ◽  
Hydrocarbon Production ◽  
Log Data ◽  
Reservoir Evaluation ◽  
Oil And Gas Reservoir

Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.

scholarly journals Geophysical imaging of porosity variations in the Danish North Sea chalk

2008 ◽  
Vol 15 ◽  
pp. 17-20 ◽  
Author(s):  
Tanni Abramovitz
Keyword(s):  
North Sea ◽  
Seismic Reflection ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Well Log ◽  
Reservoir Properties ◽  
Seismic Reflection Data ◽  
Log Data ◽  
The North ◽  
The North Sea

More than 80% of the present-day oil and gas production in the Danish part of the North Sea is extracted from fields with chalk reservoirs of late Cretaceous (Maastrichtian) and early Paleocene (Danian) ages (Fig. 1). Seismic reflection and in- version data play a fundamental role in mapping and characterisation of intra-chalk structures and reservoir properties of the Chalk Group in the North Sea. The aim of seismic inversion is to transform seismic reflection data into quantitative rock properties such as acoustic impedance (AI) that provides information on reservoir properties enabling identification of porosity anomalies that may constitute potential reservoir compartments. Petrophysical analyses of well log data have shown a relationship between AI and porosity. Hence, AI variations can be transformed into porosity variations and used to support detailed interpretations of porous chalk units of possible reservoir quality. This paper presents an example of how the chalk team at the Geological Survey of Denmark and Greenland (GEUS) integrates geological, geophysical and petrophysical information, such as core data, well log data, seismic 3-D reflection and AI data, when assessing the hydrocarbon prospectivity of chalk fields.

scholarly journals Centimeter-Scale Lithology and Facies Prediction in Cored Wells Using Machine Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Thomas Martin ◽  
Ross Meyer ◽  
Zane Jobe
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Core Material ◽  
Depth Interval ◽  
Well Log ◽  
Log Data ◽  
Core Data ◽  
Labeling Scheme

Machine-learning algorithms have been used by geoscientists to infer geologic and physical properties from hydrocarbon exploration and development wells for more than 40 years. These techniques historically utilize digital well-log information, which, like any remotely sensed measurement, have resolution limitations. Core is the only subsurface data that is true to geologic scale and heterogeneity. However, core description and analysis are time-intensive, and therefore most core data are not utilized to their full potential. Quadrant 204 on the United Kingdom Continental Shelf has publicly available open-source core and well log data. This study utilizes this dataset and machine-learning models to predict lithology and facies at the centimeter scale. We selected 12 wells from the Q204 region with well-log and core data from the Schiehallion, Foinaven, Loyal, and Alligin hydrocarbon fields. We interpreted training data from 659 m of core at the sub-centimeter scale, utilizing a lithology-based labeling scheme (five classes) and a depositional-process-based facies labeling scheme (six classes). Utilizing a “color-channel-log” (CCL) that summarizes the core image at each depth interval, our best performing trained model predicts the correct lithology with 69% accuracy (i.e., the predicted lithology output from the model is the same as the interpreted lithology) and predicts individual lithology classes of sandstone and mudstone with over 80% accuracy. The CCL data require less compute power than core image data and generate more accurate results. While the process-based facies labels better characterize turbidites and hybrid-event-bed stratigraphy, the machine-learning based predictions were not as accurate as compared to lithology. In all cases, the standard well-log data cannot accurately predict lithology or facies at the centimeter level. The machine-learning workflow developed for this study can unlock warehouses full of high-resolution data in a multitude of geological settings. The workflow can be applied to other geographic areas and deposit types where large quantities of photographed core material are available. This research establishes an open-source, python-based machine-learning workflow to analyze open-source core image data in a scalable, reproducible way. We anticipate that this study will serve as a baseline for future research and analysis of borehole and core data.

Seismic Inversion Through the Application of Machine Learning Algorithms

2020 ◽  
Author(s):  
A. Smorodin ◽  
G. Shishaev ◽  
A. Volkova ◽  
D. Egorov
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Seismic Inversion ◽  
Machine Learning Algorithms

NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTIFICATION AND REPAIR

2021 ◽  
Author(s):  
Ryan Banas ◽  
◽  
Andrew McDonald ◽  
Tegwyn Perkins ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Unsupervised Learning ◽  
Learning Algorithms ◽  
Well Logs ◽  
Quality Data ◽  
Well Log ◽  
Log Data ◽  
Petrophysical Analysis

Subsurface analysis-driven field development requires quality data as input into analysis, modelling, and planning. In the case of many conventional reservoirs, pay intervals are often well consolidated and maintain integrity under drilling and geological stresses providing an ideal logging environment. Consequently, editing well logs is often overlooked or dismissed entirely. Petrophysical analysis however is not always constrained to conventional pay intervals. When developing an unconventional reservoir, pay sections may be comprised of shales. The requirement for edited and quality checked logs becomes crucial to accurately assess storage volumes in place. Edited curves can also serve as inputs to engineering studies, geological and geophysical models, reservoir evaluation, and many machine learning models employed today. As an example, hydraulic fracturing model inputs may span over adjacent shale beds around a target reservoir, which are frequently washed out. These washed out sections may seriously impact logging measurements of interest, such as bulk density and acoustic compressional slowness, which are used to generate elastic properties and compute geomechanical curves. Two classifications of machine learning algorithms for identifying outliers and poor-quality data due to bad hole conditions are discussed: supervised and unsupervised learning. The first allows the expert to train a model from existing and categorized data, whereas unsupervised learning algorithms learn from a collection of unlabeled data. Each classification type has distinct advantages and disadvantages. Identifying outliers and conditioning well logs prior to a petrophysical analysis or machine learning model can be a time-consuming and laborious process, especially when large multi-well datasets are considered. In this study, a new supervised learning algorithm is presented that utilizes multiple-linear regression analysis to repair well log data in an iterative and automated routine. This technique allows outliers to be identified and repaired whilst improving the efficiency of the log data editing process without compromising accuracy. The algorithm uses sophisticated logic and curve predictions derived via multiple linear regression in order to systematically repair various well logs. A clear improvement in efficiency is observed when the algorithm is compared to other currently used methods. These include manual processing by a petrophysicist and unsupervised outlier detection methods. The algorithm can also be leveraged over multiple wells to produce more generalized predictions. Through a platform created to quickly identify and repair invalid log data, the results are controlled through input and supervision by the user. This methodology is not a direct replacement of an expert interpreter, but complementary by allowing the petrophysicist to leverage computing power, improve consistency, reduce error and improve turnaround time.

Petrophysical seismic inversion conditioned to well-log data: Methods and application to a gas reservoir

Geophysics ◽  
2009 ◽  
Vol 74 (2) ◽  
pp. O1-O15 ◽  
Author(s):  
Miguel Bosch ◽  
Carla Carvajal ◽  
Juan Rodrigues ◽  
Astrid Torres ◽  
Milagrosa Aldana ◽  
...  
Keyword(s):  
Multiscale Model ◽  
Problem Formulation ◽  
Monte Carlo Sampling ◽  
Seismic Inversion ◽  
Well Log ◽  
Random Component ◽  
Reservoir Properties ◽  
Gas Reservoir ◽  
Statistical Characterization ◽  
Log Data

Hydrocarbon reservoirs are characterized by seismic, well-log, and petrophysical information, which is dissimilar in spatial distribution, scale, and relationship to reservoir properties. We combine this diverse information in a unified inverse-problem formulation using a multiproperty, multiscale model, linking properties statistically by petrophysical relationships and conditioning them to well-log data. Two approaches help us: (1) Markov-chain Monte Carlo sampling, which generates many reservoir realizations for estimating medium properties and posterior marginal probabilities, and (2) optimization with a least-squares iterative technique to obtain the most probable model configuration. Our petrophysical model, applied to near-vertical-anglestackedseismic data and well-log data from a gas reservoir, includes a deterministic component, based on a combination of Wyllie and Wood relationships calibrated with the well-log data, and a random component, based on the statistical characterization of the deviations of well-log data from the petrophysical transform. At the petrophysical level, the effects of porosity and saturation on acoustic impedance are coupled; conditioning the inversion to well-log data helps resolve this ambiguity. The combination of well logs, petrophysics, and seismic inversion builds on the corresponding strengths of each type of information, jointly improving (1) cross resolution of reservoir properties, (2) vertical resolution of property fields, (3) compliance to the smooth trend of property fields, and (4) agreement with well-log data at well positions.

Predicting Porosity and Water Saturation from Well-Log Data Using Probabilistic Multi-Task Neural Network with Normalizing Flows

2021 ◽  
Author(s):  
Jinwoo Lee ◽  
Minsu Kwon ◽  
Youngjun Hong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Water Saturation ◽  
Learning Algorithms ◽  
Core Sample ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Well Log ◽  
Sample Data

Abstract In the oil and gas exploration process, understanding the hydrocarbon distribution of a reservoir is important. Well-log and core sample data such as porosity and water saturation are widely used for this purpose. With porosity and water saturation, we can calculate hydrocarbon volume more accurately than using well-log solely. However, as obtaining core sample data is expensive and time-consuming, predicting it with well-log can be a valuable solution for early-stage exploration since acquiring well-log is relatively economic and swift. Recently, numerous studies applied machine learning algorithms to predict core data from well-log. To the best of our knowledge, most works provide point estimation without probabilistic distribution modeling. In this paper, we developed a probabilistic deep neural network to provide uncertainty via confidence interval. Besides, we employed normalizing flows and multi-task learning to improve prediction accuracy. With this approach, we present the model's uncertainty that can be reliable information for decision making. Furthermore, we demonstrate our model outperforms other supervised machine learning algorithms regards to prediction accuracy.

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