scholarly journals Geothermal Gradients in the Upper Amazon Basin derived from BHT data

2021 ◽  
Vol 4 (1) ◽  
pp. 85-94
Author(s):  
Diego Paul Barba ◽  
Roberto Barragán ◽  
Jonathan Gallardo ◽  
Alfonso Salguero
Keyword(s):  
Amazon Basin ◽  
Regional Distribution ◽  
Foreland Basin ◽  
Geothermal Gradient ◽  
Analysis Data ◽  
Wide Distribution ◽  
Sufficient Information ◽  
Southeastern Part ◽  
Data Set ◽  
Shallow Subsurface

The Upper Amazon Basin (UAB), present foredeep of the sub Andean retro-foreland basin. It comprises Putumayo area (southeastern part of Colombia), Oriente area (eastern Ecuador) and Marañón area (northeastern part of Peru). Bottom Hole Temperature (BHT) from a regional well log database (1076 wells and 2957 logs) were analyzed and data discriminated according to drilling operations (i.e., log acquisition operations, cementing, formation test, tools misreading values), topography and shallow subsurface weathering conditions (i.e., temperature data from wells with depths below 305 m. were avoided). A new normalized database has been setup (1021 wells and 1399 logs). Analysis of this data set has allowed better understanding of the regional distribution of geothermal gradient variations within the study area. The results indicate mean uncorrected geothermal gradient (UCGG) for the UAB of 20.4 °C/km. The UCGG is a first approach based on well data with sufficient information and is useful for comparison purposes with other basin where corrected data is limited. In addition, a new computer-generated contour Geothermal Gradient Map (GGM) has been created, using 56 locations (for which number of BHT at different depths ≥ 3, and temperatures in the range 23.4 to 44.4 °C). As for the locations 2 are in Colombia, 28 in Ecuador and 26 in Peru. This map is useful in analysis data of UCGG due to its wide distribution along the basin. Finally, correction based on Horner´s method was applied to these datasets (where number of BHT ≥3 at the same depth; time since circulation - TSC incremental), obtaining a Corrected Geothermal Gradient (HCGG) of 22.9 °C/km (46 wells and 153 logs). We recommend the use of this gradient for comparative reference purposes.

scholarly journals Method for evaluation of the cleanliness grade of coal resources in the Huainan Coalfield, Anhui, China: a case study

2021 ◽  
Author(s):  
Guangqing Hu ◽  
Guijian Liu ◽  
Dun Wu ◽  
Wenyong Zhang ◽  
Biao Fu
Keyword(s):  
Trace Elements ◽  
Coal Seam ◽  
Regional Distribution ◽  
Large Data ◽  
Index Method ◽  
Data Set ◽  
Poor Grade ◽  
Huainan Coalfield ◽  
Coal Grade ◽  
Grade Iii

AbstractBased on analysis of a large data set and supplementary sampling and analysis for hazardous trace elements in coal samples from the Huainan Coalfield, a generalized contrast-weighted scale index method was used to establish a model to evaluate the grade of coal cleanliness and its regional distribution in the main coal seam (No. 13-1) The results showed that: (1) The contents of Cr, Mn and Ni in the coal seam are relatively high and the average values are greater than 20 μg/g. The contents of Se and Hg are at a high level while most other trace elements are at normal levels. (2) The cleanliness grade of the coal seam is mainly grade III–IV, which corresponds to a relatively good-medium coal cleanliness grade. However, some parts of the seam are at grade V (relatively poor coal cleanliness). (3) Coal of relatively good cleanliness grade (grade III) is distributed mainly in the regions corresponding to the Zhuji-Dingji-Gubei coal mines and in the eastern periphery of the Panji coal mine. Coal of medium cleanliness (grade IV) is distributed mainly in the regions of the Panji-Xiejiaji and Kouzidong coalmines. Relatively poor grade coal (grade V) is distributed in the southwest regions of the coalfield and the contents of Cr, As and Hg in coal collected from the relatively poor coal cleanliness regions often exceed the regulatory standards for the maximum concentration limits.

scholarly journals Spatiotemporal variations of nitrogen isotopic records in the Arabian Sea

2015 ◽  
Vol 12 (1) ◽  
pp. 1-14 ◽  
Author(s):  
S.-J. Kao ◽  
B.-Y. Wang ◽  
L.-W. Zheng ◽  
K. Selvaraj ◽  
S.-C. Hsu ◽  
...  
Keyword(s):  
Dissolved Oxygen ◽  
Arabian Sea ◽  
Euphotic Zone ◽  
Southeastern Part ◽  
Data Set ◽  
Global Coherence ◽  
One Step ◽  
Isotopic Records ◽  
Bottom Depth ◽  
Reported Data

Abstract. Available reports of dissolved oxygen, δ15N of nitrate (δ 15NNO3) and δ15N of total nitrogen (δ15Nbulk) for trap material and surface/downcore sediments from the Arabian Sea (AS) were synthesized to explore the AS' past nitrogen dynamics. Based on 25 μmol kg−1 dissolved oxygen isopleth at a depth of 150 m, we classified all reported data into northern and southern groups. By using δ15Nbulk of the sediments, we obtained geographically distinctive bottom-depth effects for the northern and southern AS at different climate stages. After eliminating the bias caused by bottom depth, the modern-day sedimentary δ15Nbulk values largely reflect the δ15NNO3 supply from the bottom of the euphotic zone. Additionally to the data set, nitrogen and carbon contents vs. their isotopic compositions of a sediment core (SK177/11) collected from the most southeastern part of the AS were measured for comparison. We found a one-step increase in δ15Nbulk starting at the deglaciation with a corresponding decrease in δ13CTOC similar to reports elsewhere revealing a global coherence. By synthesizing and reanalyzing all reported down core δ15Nbulk, we derived bottom-depth correction factors at different climate stages, respectively, for the northern and southern AS. The diffusive sedimentary δ15Nbulk values in compiled cores became confined after bias correction revealing a more consistent pattern except recent 6 ka. Such high similarity to the global temporal pattern indicates that the nitrogen cycle in the entire AS had responded to open-ocean changes until 6 ka BP. Since 6 ka BP, further enhanced denitrification (i.e., increase in δ15Nbulk) in the northern AS had occurred and was likely driven by monsoon, while, in the southern AS, we observed a synchronous reduction in δ15Nbulk, implying that nitrogen fixation was promoted correspondingly as the intensification of local denitrification at the northern AS basin.

scholarly journals Deep Geothermal Heating Potential for the Communities of the Western Canadian Sedimentary Basin

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 706
Author(s):  
Jacek Majorowicz ◽  
Stephen E. Grasby
Keyword(s):  
British Columbia ◽  
Northwest Territories ◽  
Geothermal Energy ◽  
Sedimentary Basin ◽  
Electrical Power ◽  
Foreland Basin ◽  
Geothermal Gradient ◽  
Energy Potential ◽  
Western Canada Sedimentary Basin ◽  
Geothermal Heating

We summarize the feasibility of using geothermal energy from the Western Canada Sedimentary Basin (WCSB) to support communities with populations >3000 people, including those in northeastern British Columbia, southwestern part of Northwest Territories (NWT), southern Saskatchewan, and southeastern Manitoba, along with previously studied communities in Alberta. The geothermal energy potential of the WCSB is largely determined by the basin’s geometry; the sediments start at 0 m thickness adjacent to the Canadian shield in the east and thicken to >6 km to the west, and over 3 km in the Williston sub-basin to the south. Direct heat use is most promising in the western and southern parts of the WCSB where sediment thickness exceeds 2–3 km. Geothermal potential is also dependent on the local geothermal gradient. Aquifers suitable for heating systems occur in western-northwestern Alberta, northeastern British Columbia, and southwestern Saskatchewan. Electrical power production is limited to the deepest parts of the WCSB, where aquifers >120 °C and fluid production rates >80 kg/s occur (southwestern Northwest Territories, northwestern Alberta, northeastern British Columbia, and southeastern Saskatchewan. For the western regions with the thickest sediments, the foreland basin east of the Rocky Mountains, estimates indicate that geothermal power up to 2 MWel. (electrical), and up to 10 times higher for heating in MWth. (thermal), are possible.

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

scholarly journals The Development of a Comprehensive Data Set for Systematic Studies of Machine Translation

2021 ◽  
pp. 248-262
Author(s):  
Jörg Tiedemann
Keyword(s):  
Machine Translation ◽  
Training Data ◽  
Sufficient Information ◽  
Data Set ◽  
Neural Machine Translation ◽  
High Resource ◽  
Language Groups ◽  
Comprehensive Data ◽  
Parallel Data ◽  
Initial Results

This paper presents our on-going efforts to develop a comprehensive data set and benchmark for machine translation beyond high-resource languages. The current release includes 500GB of compressed parallel data for almost 3,000 language pairs covering over 500 languages and language variants. We present the structure of the data set and demonstrate its use for systematic studies based on baseline experiments with multilingual neural machine translation between Finno-Ugric languages and other language groups. Our initial results show the capabilities of training effective multilingual translation models with skewed training data but also stress the shortcomings with low-resource settings and the difficulties to obtain sufficient information through straightforward transfer from related languages.

scholarly journals Corrigendum: Nutritional Evaluation and Optimisation in Neonates (NEON) trial of amino acid regimen and intravenous lipid composition in preterm parenteral nutrition: a randomised double-blind controlled trial

2017 ◽  
Vol 3 (2) ◽  
pp. 81-82
Author(s):  
Sabita Uthaya ◽  
Xinxue Liu ◽  
Daphne Babalis ◽  
Caroline Dore ◽  
Jane Warwick ◽  
...  
Keyword(s):  
Controlled Trial ◽  
Analysis Data ◽  
Secondary Outcome ◽  
Final Report ◽  
Serious Adverse Events ◽  
Double Blind ◽  
Correct Treatment ◽  
Data Set ◽  
Study Results ◽  
Made In

Abstract During the uploading of data for submission to the EudraCT results database, a discrepancy was identified. It was noted that the number of deaths per group was not consistent with the number in the final report and trial publication. This discrepancy was found to relate to two randomisation numbers. During the trial, the randomisation database had been held separately from the trial database, with manual transcription of randomisation numbers from the randomisation database to the trial database. Two randomisation numbers had been entered incorrectly into the trial database and, although this was documented at the time, the correction had not been made in the analysis data set. The two infants in question received the correct treatment in accordance with their allocation, but were analysed according to the wrong treatment group. Following the identification of this error, all analyses were repeated. It was confirmed that this error had a negligible impact on the study results. Furthermore, the two infants in question had not been included in the primary and secondary outcome analyses, as one had died and the other had withdrawn prior to the primary end-point assessment, so the key study outcomes remain unchanged. The only changes to the results are in the number of serious adverse events and minor changes to the data in demographics tables mostly affecting decimal points and the CONSORT diagram. Our interpretation of the study results remains unchanged.

scholarly journals The SISAL database: a global resource to document oxygen and carbon isotope records from speleothems

2018 ◽  
Author(s):  
Kamolphat Atsawawaranunt ◽  
Laia Comas-Bru ◽  
Sahar Amirnezhad Mozhdehi ◽  
Michael Deininger ◽  
Sandy P. Harrison ◽  
...  
Keyword(s):  
Working Group ◽  
Climate Models ◽  
Full Range ◽  
Global Changes ◽  
Sufficient Information ◽  
Data Set ◽  
Oxygen And Carbon Isotope ◽  
Cave System ◽  
Out Of Sample ◽  
Age Model

Abstract. Stable isotope records from speleothems provide information on past climate changes, most particularly information that can be used to reconstruct past changes in precipitation and atmospheric circulation. These records are increasingly being used to provide “out-of-sample” evaluations of isotope-enabled climate models. SISAL (Speleothem Isotope Synthesis and Analysis) is an international working group of the Past Global Changes (PAGES) project. The working group aims to provide a comprehensive compilation of speleothem isotope records for climate reconstruction and model evaluation. The SISAL database contains data for individual speleothems, grouped by cave system. Stable isotopes of oxygen and carbon (δ18O, δ13C) measurements are referenced by distance from the top or youngest part of the speleothem. Additional tables provide information on dating, including information on the dates used to construct the original age model and sufficient information to assess the quality of each data set and to erect a standardized chronology across different speleothems. The metadata table provides location information, information about the full range of measurements carried out on each speleothem and information about the cave system that is relevant to the interpretation of the records, as well as citations for both publications and archived data. The compiled data are available at http://dx.doi.org/10.17864/1947.139.

Overall Survival Rate Analysis For Risk Factors In Women With Neuroendocrine Carcinoma of The Cervix Using Surveillance, Epidemiology, And End Results (SEER) Database

2021 ◽  
Author(s):  
Ziran Yin ◽  
Xiumin Huang
Keyword(s):  
Overall Survival ◽  
Lymph Nodes ◽  
Neuroendocrine Carcinoma ◽  
Cox Regression ◽  
Analysis Data ◽  
Seer Database ◽  
Regional Lymph Nodes ◽  
Data Set ◽  
Cox Models ◽  
Cox Regression Analysis

Abstract Background: Neuroendocrine carcinoma of the cervix is rare and aggressive disease, of which prognosis information and the effectiveness of the therapies is unclear.Methods: A retrospective study using data from the SEER database for the first diagnosed Neuroendocrine carcinoma of the cervix patients was conducted. We performed univariate and multivariate Cox models to screen for independent prognostic factors for overall survival. Subgroup analysis and sensitive analysis were performed for further study, then again univariate and multivariate analyses of Cox regression analysis were performed based on the sensitivity analysis data set.Results: A total of 250 Neuroendocrine carcinoma of the cervix cases was included, tumor subtype, age, marriage, race, number of regional lymph nodes, number of positive lymph nodes, radiotherapy, surgery, and FIGO stage were all factors affecting OS, and multivariate analysis identified FIGO staging (HR, 2.4; 95% CI, 1.505-3.828, P < 0.001) and surgery (HR, 0.467; 95% CI, 0.358-0.609, P < 0.001) treatment as independent indicators. With respect to the factors associated with treatments, we found that patients who underwent surgery (yes vs. no vs. unknown) or radiation (yes vs. no) experienced prolonged survival, both P < 0.001Conclusions: Our investigation shows that for patients with NECC surgery seems to be the effective treatment. Chemotherapy cannot improve the prognosis of NECC patients, and the effectiveness of radiation should be further verified.

Categorization of Data Clustering Techniques

2008 ◽  
pp. 568-577
Author(s):  
Baoying Wang ◽  
Imad Rahal ◽  
Richard Leipold
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Data Clustering ◽  
Analysis Data ◽  
Discovery Process ◽  
Data Set ◽  
Market Basket ◽  
Clustering Techniques ◽  
Data Points ◽  
Class Labels

Data clustering is a discovery process that partitions a data set into groups (clusters) such that data points within the same group have high similarity while being very dissimilar to points in other groups (Han & Kamber, 2001). The ultimate goal of data clustering is to discover natural groupings in a set of patterns, points, or objects without prior knowledge of any class labels. In fact, in the machine-learning literature, data clustering is typically regarded as a form of unsupervised learning as opposed to supervised learning. In unsupervised learning or clustering, there is no training function as in supervised learning. There are many applications for data clustering including, but not limited to, pattern recognition, data analysis, data compression, image processing, understanding genomic data, and market-basket research.

Detection of sharp lateral discontinuities through the analysis of surface-wave propagation

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. EN77-EN90 ◽  
Author(s):  
Paolo Bergamo ◽  
Laura Valentina Socco
Keyword(s):  
Surface Wave ◽  
Fault Location ◽  
Synthetic Data ◽  
Finite Element Method Simulation ◽  
Fault System ◽  
Energy Concentration ◽  
Data Sets ◽  
Data Set ◽  
Shallow Subsurface ◽  
Velocity Models

Surface-wave (SW) techniques are mainly used to retrieve 1D velocity models and are therefore characterized by a 1D approach, which might prove unsatisfactory when relevant 2D effects are present in the investigated subsurface. In the case of sharp and sudden lateral heterogeneities in the subsurface, a strategy to tackle this limitation is to estimate the location of the discontinuities and to separately process seismic traces belonging to quasi-1D subsurface portions. We have addressed our attention to methods aimed at locating discontinuities by identifying anomalies in SW propagation and attenuation. The considered methods are the autospectrum computation and the attenuation analysis of Rayleigh waves (AARW). These methods were developed for purposes and/or scales of analysis that are different from those of this work, which aims at detecting and characterizing sharp subvertical discontinuities in the shallow subsurface. We applied both methods to two data sets, synthetic data from a finite-element method simulation and a field data set acquired over a fault system, both presenting an abrupt lateral variation perpendicularly crossing the acquisition line. We also extended the AARW method to the detection of sharp discontinuities from large and multifold data sets and we tested these novel procedures on the field case. The two methods are proven to be effective for the detection of the discontinuity, by portraying propagation phenomena linked to the presence of the heterogeneity, such as the interference between incident and reflected wavetrains, and energy concentration as well as subsequent decay at the fault location. The procedures we developed for the processing of multifold seismic data set showed to be reliable tools in locating and characterizing subvertical sharp heterogeneities.

Sign in / Sign up

Export Citation Format

Share Document