Machine Learning-based Approach for Automated Identification of Produced Water Types from Conventional and Unconventional Reservoirs

2019 ◽  
Author(s):  
P. Birkle ◽  
M. Zouch ◽  
M. Alzaqebah ◽  
M. Alwohaibi
Keyword(s):  
Machine Learning ◽  
Produced Water ◽  
Unconventional Reservoirs ◽  
Automated Identification ◽  
Water Types

A machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI

2021 ◽  
pp. 100577
Author(s):  
Edson Filisbino Freire da Silva ◽  
Evlyn Márcia Leão de Moraes Novo ◽  
Felipe de Lucia Lobo ◽  
Cláudio Clemente Faria Barbosa ◽  
Carolline Tressmann Cairo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Water Types ◽  
Machine Learning Approach ◽  
Sentinel 2

Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia

2021 ◽  
Author(s):  
Maaruf Hussain ◽  
Abduljamiu Amao ◽  
Khalid Al-Ramadan ◽  
Sunday Olatunji ◽  
Ardiansyah Negara
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Saudi Arabia ◽  
Spatial Distribution ◽  
Chemical Composition ◽  
Rock Strength ◽  
Machine Learning Algorithms ◽  
Unconventional Reservoirs ◽  
Geochemical Properties ◽  
Rock Mechanical Properties

Abstract The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.

scholarly journals False Positive RFID Detection Using Classification Models

2019 ◽  
Vol 9 (6) ◽  
pp. 1154 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Bohan Yoon ◽  
Jongtae Rhee
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Real Time ◽  
Outlier Detection ◽  
Radio Frequency Identification ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Automated Identification ◽  
Rfid Data

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

A Leap into Automation for Advanced Fracture Characterization

2021 ◽  
Author(s):  
Radhika Patro ◽  
Manas Mishra ◽  
Hemlata Chawla ◽  
Sambhaji Devkar ◽  
Mrinal Sinha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fracture Network ◽  
Unconventional Reservoirs ◽  
Machine Learning Techniques ◽  
Coal Bed ◽  
Fracture Characterization ◽  
Learning Techniques ◽  
Macro Scale ◽  
Porosity And Permeability ◽  
Depth Of Investigation

Abstract Fractures are the prime conduits of flow for hydrocarbons in reservoir rocks. Identification and characterization of the fracture network yields valuable information for accurate reservoir evaluation. This study aims to portray the benefits and limitations for various existing fracture characterization methods and define strategic workflows for automated fracture characterization targeting both conventional and unconventional reservoirs separately. While traditional seismic provides qualitative information of fractures and faults on a macro scale, acoustics and other petrophysical logs provide a more comprehensive picture on a meso and micro level. High resolution image logs, with shallow depth of investigation are considered the industry standard for analysis of fractures. However, it is imperative to understand the framework of fracture in both near and far field. Various reservoir-specific collaborative workflows have been elucidated for a consistent evaluation of fracture network, results of which are further segregated using class-based machine learning techniques. This study embarks on understanding the critical requirements for fracture characterization in different lithological settings. Conventional reservoirs have good intrinsic porosity and permeability, yet presence of fractures further enhances the flow capacity. In clastic reservoirs, fractures provide an additional permeability assist to an already producible reservoir. In carbonate reservoirs, overall reservoir and production quality exclusively depends on presence of extensive fracture network as it quantitatively controls the fluid flow interactions among otherwise isolated vugs. Devoid of intrinsic porosity and permeability, the presence of open-extensive fractures is even more critical in unconventional reservoirs such as basement, shale-gas/oil and coal-bed methane, since it demarcates the reservoir zone and defines the economic viability for hydrocarbon exploration in reservoirs. Different forward modeling approaches using the best of conventional logs, borehole images, acoustic data (anisotropy analysis, borehole reflection survey and stoneley waveforms) and magnetic resonance logs have been presented to provide reservoir-specific fracture characterization. Linking the resolution and depth of investigation of different available techniques is vital for the determination of openness and extent of the fractures into the formation. The key innovative aspect of this project is the emphasis on an end-to-end suitable quantitative analysis of flow contributing fractures in different conventional and unconventional reservoirs. Successful establishment of this approach capturing critical information will be the stepping-stone for developing machine learning techniques for field level assessment.

scholarly journals Seek and learn: Automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning

2020 ◽  
Vol 11 (12) ◽  
pp. 1639-1651
Author(s):  
Pritish Chakravarty ◽  
Gabriele Cozzi ◽  
Hooman Dejnabadi ◽  
Pierre‐Alexandre Léziart ◽  
Marta Manser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Animal Behaviour ◽  
Automated Identification ◽  
Acceleration Data

scholarly journals Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Yanjie Li ◽  
Mahmoud Al-Sarayreh ◽  
Kenji Irie ◽  
Deborah Hackell ◽  
Graeme Bourdot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Support Vector ◽  
Classification Models ◽  
Imaging Data ◽  
Weed Species ◽  
Automated Identification ◽  
Standard Normal Variate ◽  
Californian Thistle ◽  
Ranunculus Acris

Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70–100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

scholarly journals Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling

2020 ◽  
Author(s):  
Ioan-Bogdan Magdau ◽  
Thomas Miller
Keyword(s):  
Machine Learning ◽  
Molecular Mechanisms ◽  
Ion Solvation ◽  
Automated Identification ◽  
Chemical Systems ◽  
Machine Learning Approach ◽  
Rapid Diversification ◽  
Li Ion ◽  
Phase Systems

<div>Automated identification and classification of ion solvation sites in diverse chemical systems will improve the understanding and design of polymer electrolytes for battery applications. We introduce a machine learning approach to classify and characterize ion solvation environments based on feature vectors extracted from all-atom simulations. This approach is demonstrated in poly(3,4-propylenedioxythiophene), which is a promising candidate polymer binder for Li-ion batteries. In the dry polymer, four</div><div>distinct Li+ solvation environments are identified close to the backbone of the polymer. Upon swelling of the polymer with propylene carbonate solvent, the nature of Li+ solvation changes dramatically, featuring a rapid diversification</div><div>of solvation environments. This application of machine learning can be generalized to other polymer condensed-phase systems to elucidate the molecular mechanisms underlying ion solvation.</div>

scholarly journals Thinking like a naturalist: enhancing computer vision of citizen science images by harnessing contextual data

2019 ◽  
Author(s):  
J. Christopher D. Terry ◽  
Helen E. Roy ◽  
Tom A. August
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Contextual Information ◽  
Network Models ◽  
List Type ◽  
British Isles ◽  
Automated Identification ◽  
Neural Network Models ◽  
Contextual Data

AbstractThe accurate identification of species in images submitted by citizen scientists is currently a bottleneck for many data uses. Machine learning tools offer the potential to provide rapid, objective and scalable species identification for the benefit of many aspects of ecological science. Currently, most approaches only make use of image pixel data for classification. However, an experienced naturalist would also use a wide variety of contextual information such as the location and date of recording.Here, we examine the automated identification of ladybird (Coccinellidae) records from the British Isles submitted to the UK Ladybird Survey, a volunteer-led mass participation recording scheme. Each image is associated with metadata; a date, location and recorder ID, which can be cross-referenced with other data sources to determine local weather at the time of recording, habitat types and the experience of the observer. We built multi-input neural network models that synthesise metadata and images to identify records to species level.We show that machine learning models can effectively harness contextual information to improve the interpretation of images. Against an image-only baseline of 48.2%, we observe a 9.1 percentage-point improvement in top-1 accuracy with a multi-input model compared to only a 3.6% increase when using an ensemble of image and metadata models. This suggests that contextual data is being used to interpret an image, beyond just providing a prior expectation. We show that our neural network models appear to be utilising similar pieces of evidence as human naturalists to make identifications.Metadata is a key tool for human naturalists. We show it can also be harnessed by computer vision systems. Contextualisation offers considerable extra information, particularly for challenging species, even within small and relatively homogeneous areas such as the British Isles. Although complex relationships between disparate sources of information can be profitably interpreted by simple neural network architectures, there is likely considerable room for further progress. Contextualising images has the potential to lead to a step change in the accuracy of automated identification tools, with considerable benefits for large scale verification of submitted records.

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