Selecting optimum log measurements for hydraulic fracturing

2016 ◽  
Vol 4 (2) ◽  
pp. SF125-SF135
Author(s):  
Mehdi E. Far ◽  
John A. Quirein ◽  
Natasa Mekic
Keyword(s):  
Hydraulic Fracturing ◽  
Gamma Ray ◽  
Correlation Coefficients ◽  
Effective Porosity ◽  
Eagle Ford ◽  
Industry Standard ◽  
Neutron Porosity ◽  
Using Data ◽  
Mean Square Errors ◽  
Selection Of

We have developed a statistical method for investigating the importance of different log measurements for picking the best zones for hydraulic fracturing. We have determined the method’s applicability using data from unconventional reservoirs (Eagle Ford, Haynesville, Barnett, and a reservoir from the Middle East). The analysis began with single log measurements (e.g., gamma ray [GR], compressional and shear sonic [DTC and DTS], and spectral gamma ray [SGR], which could measure the radioactivity of uranium [U], potassium [K], and thorium [Th]). Other types of measurements, including density (RhoB), neutron porosity (NPHI), and resistivity, were added to obtain more complex logging suites. These log measurements were the inputs for this analysis. Each input combination was referred to as a “scenario.” Parameters such as effective porosity (PhiE), brittleness, total organic carbon (TOC), production index (PI), and fracture index (FI) were referred to as the outputs for the analysis. We have investigated linear and nonlinear combinations of the inputs to predict the outputs. Various scenarios, beginning with the simplest cases and ending with the most complete combination, were tested. The selection of log combinations was either based on the importance of individual logs or on industry-standard combinations (such as triple and quad combos). For each scenario, we computed correlation coefficients and root-mean-square errors of predicting the output parameters. The prediction accuracies generally increased as a result of increasing the number of input logs. Our analysis clearly found the importance of using SGR (for PI and FI prediction) and resistivity (for TOC prediction) logs. Based on comparison of the reconstruction results, actual values, and correlation coefficients/errors, we ranked the log combinations for predicting/modeling a specific parameter. The most challenging properties to model included TOC, PhiE, PI, and FI; the easiest properties to predict were brittleness and Young’s modulus.

scholarly journals Management of an Industry Standard Class in Vocational High Schools

2018 ◽  
Vol 24 (1) ◽  
pp. 46-51
Author(s):  
Suroto Suroto ◽  
Nguyen Tien Hung
Keyword(s):  
High Schools ◽  
Qualitative Study ◽  
Teaching And Learning ◽  
The Third ◽  
Industry Partner ◽  
Industry Standard ◽  
Learning Evaluation ◽  
Vocational High Schools ◽  
Using Data ◽  
Selection Of

To remove a growing gap between students’ skills received in vocational high schools and real demands in the workforce, industries should be actively involved not only as external users but to work in curriculum development and learning evaluation. This study describes the process of planning, implementation, and supervision of an industry standard class resulted from collaboration between the school and the industry. This study was a qualitative study using data collection techniques of interviews, observation and documentation. The results revealed (1) the industry and the school were partners in planning the industry standard class including development of curriculum, facilities, infrastructure, teachers, and materials, (2) implementation of the class included theoritical and practical learning, and industry practices, (3) supervision was performed by the industry partner administrating industry standard competency tests, and (4) management of the class consisted of three sequenced stages namely selection of students in the third semester, implementation of industry standardized teaching and learning process from the third semester to the sixth semester, and a competency test in the sixth semester.

scholarly journals The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

2021 ◽  
Vol 10 (1) ◽  
pp. 9-17
Author(s):  
Sudarmaji Saroji ◽  
Ekrar Winata ◽  
Putra Pratama Wahyu Hidayat ◽  
Suryo Prakoso ◽  
Firman Herdiansyah
Keyword(s):  
Machine Learning ◽  
Gamma Ray ◽  
Distribution Patterns ◽  
Effective Porosity ◽  
Depositional Environments ◽  
Beach Sand ◽  
Support Vector ◽  
Data Sets ◽  
Neutron Porosity ◽  
Lithofacies Classification

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns.

scholarly journals Stoneley Wave Predicted Permeability and Electrofacies Correlation in the Bangestan Reservoir, Mansouri Oilfield, SW Iran

2018 ◽  
Vol 57 (2) ◽  
Author(s):  
Bahman Soleimani ◽  
Mehrdad Moradi ◽  
Ali Ghabeishavi
Keyword(s):  
Reservoir Characterization ◽  
Gamma Ray ◽  
Water Saturation ◽  
Effective Porosity ◽  
Stoneley Wave ◽  
Stoneley Waves ◽  
Rock Types ◽  
The Matrix ◽  
Sw Iran ◽  
Neutron Porosity

 Reservoir characterization is one of the most important goals for the development of any oilfield. Determination of permeability and rock types are of prime importance to judge reservoir quality. In this research, Stoneley waves from dipole sonic tools were used in order to discover changes in permeability in the Bangestan reservoir, Mansouri oilfield. Index (tortuosity) could be estimated by Stoneley waves. After comparing the permeability resulting from Stoneley waves, cores and the Timur method, it was concluded that all the three permeabilities were very similar. The core porosity and effective porosity from the analysis of well logs were found to match as well. Electrofacies (EF) method, as a clustering method, was utilized to find rock types in order to define reservoir and non-reservoir zones. Simultaneous with EF clustering, gamma ray, neutron porosity, density, sonic, water saturation and porosity (PHIE) data from 78 wells were also considered and interpreted. Nine clusters were defined as a result of the analysis, being reduced to only four clusters after applying PC (capillary pressure) data. Among the four clusters, clusters 1 and 2 contained more vuggy pores than the others. Fracture abundance and solution seams were observed more frequently in EF-3 as compared to other EFs. Based on the matrix type, Archie porosity classification types I and III were recognized. The pore sizes in EFs-1 and 2 were mostly of the B type while in EF-3, it was A type. The EFs generated and determined by Stoneley waves and the well log data were also compared, showing a good correlation.

IMPROVEMENT OF SPRING DURUM WHEAT SELECTION METHODOLOGIES UNDER CLIMATE CHANGE CONDITIONS

1970 ◽  
pp. 33-36
Author(s):  
А. I. Grabovets ◽  
V. P. Kadushkina ◽  
S. А. Kovalenko
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Correlation Coefficient ◽  
Harvest Index ◽  
Unit Area ◽  
Correlation Coefficients ◽  
Chemical Mutagenesis ◽  
Main Method ◽  
The Relationship ◽  
Selection Of

With the growing aridity of the climate on the Don, it became necessary to improve the methodology for conducting the  breeding of spring durum wheat. The main method of obtaining the source material remains intraspecific step hybridization. Crossings were performed between genetically distant forms, differing in origin and required traits and properties. The use of chemical mutagenesis was a productive way to change the heredity of genotypes in terms of drought tolerance. When breeding for productivity, both in dry years of research and in favorable years, the most objective markers were identified — the size of the aerial mass, the mass of grain per plant, spike, and harvest index. The magnitude of the correlation coefficients between the yield per unit area and the elements of its structure is established. It was most closely associated with them in dry years, while in wet years it decreased. Power the correlation of the characteristics of the pair - the grain yield per square meter - the aboveground biomass averaged r = 0.73, and in dry years it was higher (0.91) than in favorable ones (0.61 - 0.70) , between the harvest and the harvest index - r = 0.81 (on average). In dry years, the correlation coefficient increased to 0.92. Research data confirms the greatest importance of the mass of grain from one ear and the plant in the formation of grain yield per unit area in both dry and wet years. In dry years, the correlation coefficient between yield and grain mass per plant was on average r = 0.80; in favorable years, r = 0.69. The relationship between yield and grain mass from the ear was greater — r = 0.84 and r = 0.82, respectively. Consequently, the breeding significance of the aboveground mass and the productivity of the ear, as a criterion for the selection of the crop, especially increases in the dry years. They were basic in the selection.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

Founding Family Ownership and the Selection of Industry Specialist Auditors

2014 ◽  
Vol 28 (2) ◽  
pp. 261-276 ◽  
Author(s):  
Fei Kang
Keyword(s):  
Financial Reporting ◽  
Family Firms ◽  
Structure And Agency ◽  
Founding Family ◽  
Using Data ◽  
Dual Class Shares ◽  
Dual Class ◽  
Selection Of ◽  
Industry Specialist

SYNOPSIS This study examines how family firms' unique ownership structure and agency problems affect their selection of industry-specialist auditors. Using data from Standard & Poor's (S&P) 1500 firms, the results show that family firms are more likely to appoint industry-specialist auditors than non-family firms, which suggests that family firms have strong incentives to signal the quality of financial reporting. Additional analysis indicates that due to the potential entrenchment problems, family firms with family member CEOs or with dual-class shares have even a higher tendency to hire industry-specialist auditors to signal their disclosure quality.

scholarly journals Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 601 ◽  
Author(s):  
Marco Germanotta ◽  
Ilaria Mileti ◽  
Ilaria Conforti ◽  
Zaccaria Del Prete ◽  
Irene Aprile ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Center Of Mass ◽  
Correlation Coefficients ◽  
Biomechanical Model ◽  
Lower Limbs ◽  
Accuracy Evaluation ◽  
Postural Tasks ◽  
Single Sensor ◽  
Using Data ◽  
Displacement Based

The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.

Sensor-Based Estimation of Dim Light Melatonin Onset Using Features of Two Time Scales

2021 ◽  
Vol 2 (3) ◽  
pp. 1-15
Author(s):  
Cheng Wan ◽  
Andrew W. Mchill ◽  
Elizabeth B. Klerman ◽  
Akane Sano
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Light Exposure ◽  
Biological Activities ◽  
Second Step ◽  
Sampled Data ◽  
Dim Light Melatonin Onset ◽  
Dim Light ◽  
Using Data ◽  
Mean Square Errors

Circadian rhythms influence multiple essential biological activities, including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring human circadian phase (i.e., timing). The collection of DLMO is expensive and time consuming since multiple saliva or blood samples are required overnight in special conditions, and the samples must then be assayed for melatonin. Recently, several computational approaches have been designed for estimating DLMO. These methods collect daily sampled data (e.g., sleep onset/offset times) or frequently sampled data (e.g., light exposure/skin temperature/physical activity collected every minute) to train learning models for estimating DLMO. One limitation of these studies is that they only leverage one time-scale data. We propose a two-step framework for estimating DLMO using data from both time scales. The first step summarizes data from before the current day, whereas the second step combines this summary with frequently sampled data of the current day. We evaluate three moving average models that input sleep timing data as the first step and use recurrent neural network models as the second step. The results using data from 207 undergraduates show that our two-step model with two time-scale features has statistically significantly lower root-mean-square errors than models that use either daily sampled data or frequently sampled data.

scholarly journals Estimating Water Vapor Using Signals from Microwave Links below 25 GHz

2021 ◽  
Vol 13 (8) ◽  
pp. 1409
Author(s):  
Kun Song ◽  
Xichuan Liu ◽  
Taichang Gao ◽  
Peng Zhang
Keyword(s):  
Water Vapor ◽  
Weather Forecasting ◽  
Water Cycle ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Vapor Density ◽  
Measurement Resolution ◽  
Sensing Model ◽  
Numerical Weather Forecasting ◽  
Mean Square Errors

Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links (MLs) has been proven to be a feasible method. Similar to rainfall, water vapor also attenuates the energy of MLs. Thus, MLs also have the potential of estimating water vapor. This study proposes a method to estimate water vapor density by using the received signal level (RSL) of MLs at 15, 18, and 23 GHz, which is the first attempt to estimate water vapor by MLs below 20 GHz. This method trains a sensing model with prior RSL data and water vapor density by the support vector machine, and the model can directly estimate the water vapor density from the RSLs without preprocessing. The results show that the measurement resolution of the proposed method is less than 1 g/m3. The correlation coefficients between automatic weather stations and MLs range from 0.72 to 0.81, and the root mean square errors range from 1.57 to 2.31 g/m3. With the large availability of signal measurements from communications operators, this method has the potential of providing refined data on water vapor density, which can contribute to research on the atmospheric boundary layer and numerical weather forecasting.

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