Formation Grain Size Profile Prediction Model Considering the Longitudinal Continuity of Reservoir Using Artificial Intelligence Tools

2021 ◽  
Author(s):  
Shanshan Liu ◽  
Zhiming Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Grain Size ◽  
Prediction Model ◽  
Correlation Coefficient ◽  
Control Design ◽  
Point Mapping ◽  
Core Samples ◽  
Size Profile ◽  
Sand Control

Abstract Grain size characteristics (d50, UC) of formation sands are crucial parameters in a sand control design. UC and d50 are commonly derived from sieve or laser particle size analysis (LPSA) techniques on a limited number of core samples in the process of drilling, which cannot represent the variations of grain sizes in the formation by the limited number of core samples. Moreover, staged and hierarchic design of sand control usually needs the whole longitudinal distribution profile of grain size. The grain size characteristics of the reservoir are formed in the process of a long history and have a good correlation with the formation environment of the sediments. Sand control design can only use test well data, because of lacking actual producing position cores. The vertical and horizontal anisotropy and heterogeneity of reservoirs bring difficulties and greater risks to the design of sand control schemes. Therefore, it is very important to find a simple and effective reservoir granularity prediction method. The existing prediction models by artificial intelligence method use single point logging data as eigenvalues to predict d50 and UC without considering the longitudinal continuity of data. This paper presents an efficient solution to predict grain size profile based on conventional logging curves by using four machine learning method (ANN, Random forest, XGBoost, SVM). In order to make full use of the geological continuity of the reservoir, the longitudinal continuous points according to the spatial correlation are adopted as the machine learning feature parameters from the perspective of geological analysis and the data-driven grain size profile prediction model are established by using the logging curve trend and background information, which further improves the prediction accuracy of the model and provides basic data for sand control. The ANN model of five point mapping has the best prediction effect in predicting d50 with a highest correlation coefficient 0.819 and a lowest error MAE 9.59. The XGBoost model of five point mapping has the best prediction effect in predicting UC with a highest correlation coefficient 0.402 and a lowest error RMSE 1.15. This method has been successfully used in offshore oil field in sand control optimization.

Sedimentary environment prediction of grain-size data based on machine learning approach

2020 ◽  
Vol 8 (3) ◽  
pp. SL71-SL78
Author(s):  
Qiao Su ◽  
Yanhui Zhu ◽  
Fang Hu ◽  
Xingyong Xu
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Prediction Model ◽  
Sedimentary Environment ◽  
Machine Learning Algorithms ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Data Set ◽  
Sedimentary Environments ◽  
Size Data

Grain size is one of the most important records for sedimentary environment, and researchers have made remarkable progress in the interpretation of sedimentary environments by grain size analysis in the past few decades. However, these advances often depend on the personal experience of the scholars and combination with other methods used together. Here, we constructed a prediction model using the K-nearest neighbors algorithm, one of the machine learning methods, which can predict the sedimentary environments of one core through a known core. Compared to the results of other studies based on the comprehensive data set of grain size and four other indicators, this model achieved a high precision value only using the grain size data. We have also compared our prediction model with other mainstream machine learning algorithms, and the experimental results of six evaluation metrics shed light on that this prediction model can achieve the higher precision. The main errors of the model reflect the length of the conversation area of sedimentary environment, which is controlled by the sedimentary dynamics. This model can provide a quick comparison method of the cores in a similar environment; thus, it may point out the preliminary guidance for further study.

Artificial Intelligence Predicts Progress of Diabetic Kidney Disease-Novel Prediction Model Construction with Big Data Machine Learning

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 539-P ◽  
Author(s):  
MASAKI MAKINO ◽  
MASAKI ONO ◽  
TOSHINARI ITOKO ◽  
TAKAYUKI KATSUKI ◽  
AKIRA KOSEKI ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Kidney Disease ◽  
Prediction Model ◽  
Diabetic Kidney Disease ◽  
Model Construction ◽  
Diabetic Kidney

scholarly journals A Novel Prediction Method Based on Artificial Intelligence and Internet of Things for Detecting Coronavirus Disease (COVID-19)

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shenqi Jing ◽  
Qijie Qian ◽  
Hao She ◽  
Tao Shan ◽  
Shan Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Collection ◽  
Internet Of Things ◽  
Prediction Model ◽  
Early Stage ◽  
Data Cleaning ◽  
Machine Learning Algorithms ◽  
Tight Coupling ◽  
Novel Coronavirus

Novel coronavirus spreads fast and has a huge impact on the whole world. In light of the spread of novel coronaviruses, we develop one big data prediction model of novel coronavirus epidemic in the context of intelligent medical treatment, taking into account all factors of infection and death and implementing emerging technologies, such as the Internet of Things (IoT) and machine learning. Based on the different application characteristics of various machine learning algorithms in the medical field, we propose one artificial intelligence prediction model based on random forest. Considering the loose coupling between the data preparation stage and the model training stage, such as data collection and data cleaning in the early stage, we adopt the IoT platform technology to integrate the data collection, data cleaning, machine learning training model, and front- and back-end frameworks to ensure the tight coupling of each module. To validate the proposed prediction model, we perform the evaluation work. In addition, the performance of the prediction model is analyzed to ensure the information accuracy of the prediction platform.

scholarly journals AI and Machine Learning in Retail Pharmacy: Systematic Review of Related Literature

2018 ◽  
Vol 7 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Praveen Kumar Donepudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Prediction Model ◽  
Retail Pharmacy ◽  
Related Literature ◽  
Efficient Performance ◽  
The Future

Artificial intelligence and machine learning are the future of every field. These can be applied in any field for better or efficient performance. Both these can be used in retail pharmacy as a solution to different problems. The machine learning prediction model can help in predicting the disease of patients and it can also be used to predict the medicine for the patient. AI systems can be used to automate the tasks that will help in saving time and also the tasks will be performed by using fewer resources. 

scholarly journals A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence

2021 ◽  
Vol 9 (9) ◽  
pp. 232596712110275
Author(s):  
Iskandar Tamimi ◽  
Joaquin Ballesteros ◽  
Almudena Perez Lara ◽  
Jimmy Tat ◽  
Motaz Alaqueel ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Anterior Cruciate Ligament ◽  
Prediction Model ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Acl Injury ◽  
Learning Techniques ◽  
Testing Accuracy ◽  
Anterior Cruciate

Background: Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. Hypothesis: It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. Results: Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (–1.7° ± 4.8° vs –4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. Conclusion: A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.

scholarly journals Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e048008
Author(s):  
Gary S Collins ◽  
Paula Dhiman ◽  
Constanza L Andaur Navarro ◽  
Ji Ma ◽  
Lotty Hooft ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Prediction Model ◽  
Assessment Tool ◽  
Risk Of Bias ◽  
Reporting Guideline ◽  
Machine Learning Techniques ◽  
Quality Of Reporting ◽  
Model Studies ◽  
Equator Network

IntroductionThe Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques.Methods and analysisTRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation.Ethics and disseminationEthical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications.PROSPERO registration numberCRD42019140361 and CRD42019161764.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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