Development of an In-Tank Tuning Fork Resonator for Automated Viscosity/Density Measurements of Drilling Fluids

González, Miguel; Thiel, Tim; Gooneratne, Chinthaka P.; Adams, Robert; Powell, Chris; Magana-Mora, Arturo; Ramasamy, Jothibasu; Deffenbaugh, Max

doi:10.1109/access.2021.3050919

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…Note that model development and evaluation were conducted to simulate real-time settings to fully resemble practical implications. Additionally, the model can be easily re-trained with data captured by novel sensors, such as a tuning fork that measures the drilling fluid viscosity and density in real-time [62].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations

et al. 2021

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Drilling operations consist of breaking the rock to deepen a wellbore for oil or gas extraction. A drilling fluid, circulating from the surface through the drill pipe and from the annulus to the surface, is used to remove rock cuttings and maintain hydrostatic pressure. Drilling fluid lost circulation incidents (LCIs) are major sources of non-productive time (NPT) in drilling operations. These incidents occur due to preexisting natural fractures (vugs, caverns, etc.) and/or drilling-induced hydraulic fractures. The initiation of an LCI could lead to other hazardous drilling phenomena, such as formation influx or kick/blowout, stuck pipe incidents, among others. LCIs are typically monitored at the rig site by observing drilling fluid levels in the fluid tanks. This manual process incurs missing the occurrence or late detection of LCIs. Machine learning (ML) and deep learning (DL) classification algorithms are powerful in processing time-series data and achieving early detection of such temporal phenomena. In this study, we performed a large-scale analysis of the surface drilling and rheology data obtained from historical wells with LCIs. This analysis includes primary and secondary preprocessing steps including, aggressive sampling, feature engineering, and window normalization to derive generalizable DL models for real-time operations. Focal loss was utilized to account for data class imbalance and train robust and generalizable models. The results obtained from different ML/DL algorithms showed that one-dimensional convolutional neural network models resulted in the best performance with state-of-the-art precision, recall, and F1 scores of 87.34%, 73.40%, and 79.77%, respectively, on unseen test drilling data.

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Section: Discussionmentioning

confidence: 99%

Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations

et al. 2021

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“…2, which signifies the threshold value of shear stress required to initiate the fluid flow. The slope of the linear line is the PV, essential for wellbore hydraulics and hole cleaning [2]. This model is expressed as follows:…”

Section: Overview Of Relevant Drilling Fluids Propertiesmentioning

confidence: 99%

Drilling Fluid Properties Prediction: A Machine Learning Approach to Automate Laboratory Experiments

Aljubran

AlBahrani

Ramasamy

et al. 2022

Preprint

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Providing access to underground energy resources such as oil, gas, and geothermal energy requires drilling through subterranean rock formations. Drilling fluids are commonly used to enable this drilling process by serving a variety of functions. Chief among these is circulating into the drilled wellbore to remove the produced rock cuttings. Other functions include exerting the hydrostatic pressure necessary to prevent the flow of underground fluids into the wellbore while drilling, minimizing the invasion of solids and undesired fluids into the wellbore rock, and ensuring the fluid remains flowable within the means of the pumps available on site. To assess the ability of a drilling fluid to serve these functions, it has to have certain rheological properties, which are conventionally measured using specialized equipment. Considering that the formulation and components of these drilling fluids can vary greatly for different scenarios, the process of preparing samples and measuring their rheological properties, which is usually performed on a daily basis on a drilling site, is unavoidably time-consuming, repetitive, and error-prone. Based on this, it is apparent that there is a need for a computational model that can accurately predict drilling fluids properties based on the proposed concentration of their components without the need for further laboratory testing. This study describes a novel methodology to train a machine learning model derived from over 6,878 drilling fluid formulations to successfully predict water-based drilling fluids properties with a resulting R2 of 91.07 ± 6.35%.

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“…For this purpose, a drill bit and drillstring assembly are used to create a hole vertically or at an angle (directional drilling) targeting a hydrocarbon reservoir [46,47]. During drilling, a fluid, slurry, or mud (referred to as mud, hereafter) with different properties is circulated into the hole (through the drillstring) and back to the surface (through the annulus) for various functions, including the removal of the rock formation cuttings and for maintaining both downhole temperatures and rock formation pressures [48] (Fig. 1A).…”

Section: A Drillingmentioning

confidence: 99%

Well Control Space Out: A Deep-Learning Approach for the Optimization of Drilling Safety Operations

Magana-Mora

Affleck²,

Ibrahim

et al. 2021

IEEE Access

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As drilling of new oil and gas wells increase to meet energy demands, it is essential to optimize processes to ensure the health and safety of the crew as well as the protection of the environment. Drilling operations represent a dynamic and challenging environment with natural and mechanical factors that need to be closely managed. Well control refers to the technique employed while drilling for balancing the hydrostatic and formation pressures to prevent the influx of water, gas, or hydrocarbons that would ultimately result in an uncontrolled flow to the surface. In the event of a well control incident, the crew must take proper and prompt actions to mitigate the risks and shut-in the well. In this study, we introduce the Well Control Space Out technology, an internet-of-things (IoT) environment that couples cameras and an edge server to implement stateof-the-art deep-learning models for the real-time processing of video images recording the drillstring. The computational models automatically perform object detection to keep track of key drilling rig components. The results from the video analysis are displayed on a dashboard describing the state and steps to follow in a well control incident without the need for any time-consuming, manual calculations. The internet-of-things edge foundation laid in drilling can be seamlessly expanded to other upstream sectors, where time-sensitive, critical decisions can be made in real-time, in the field, closer to operations. Finally, this technology can be seamlessly integrated with the current technologies to develop an automated closed-loop control system.INDEX TERMS Automation, deep-learning, computer vision, edge computing, internet-of-things, oil and gas drilling, well control.

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Development of an In-Tank Tuning Fork Resonator for Automated Viscosity/Density Measurements of Drilling Fluids

Cited by 9 publications

References 18 publications

Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations

Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations

Drilling Fluid Properties Prediction: A Machine Learning Approach to Automate Laboratory Experiments

Well Control Space Out: A Deep-Learning Approach for the Optimization of Drilling Safety Operations

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