Automated Drilling Fluid Rheology Characterization with Downhole Pressure Sensor Data

2015 ◽  
Author(s):  
Ali Karimi Vajargah ◽  
Eric van Oort
Keyword(s):  
Pressure Sensor ◽  
Drilling Fluid ◽  
Sensor Data ◽  
Fluid Rheology

Interdisciplinary Approach for Wellbore Stability During Slimhole Drilling at Volga-Urals Basin Oilfield

2021 ◽  
Author(s):  
Anna Vladimirovna Norkina ◽  
Sergey Mihailovich Karpukhin ◽  
Konstantin Urjevich Ruban ◽  
Yuriy Anatoljevich Petrakov ◽  
Alexey Evgenjevich Sobolev
Keyword(s):  
Drilling Fluid ◽  
Interdisciplinary Approach ◽  
Wellbore Stability ◽  
Vertical Wells ◽  
Wellbore Instability ◽  
Water Based ◽  
Chemical Studies ◽  
Fluid Rheology ◽  
Selection Of

Abstract The design features and the need to use a water-based solution make the task of ensuring trouble-free drilling of vertical wells non-trivial. This work is an example of an interdisciplinary approach to the analysis of the mechanisms of instability of the wellbore. Instability can be caused by a complex of reasons, in this case, standard geomechanical calculations are not enough to solve the problem. Engineering calculations and laboratory chemical studies are integrated into the process of geomechanical modeling. The recommendations developed in all three areas are interdependent and inseparable from each other. To achieve good results, it is necessary to comply with a set of measures at the same time. The key tasks of the project were: determination of drilling density, tripping the pipe conditions, parameters of the drilling fluid rheology, selection of a system for the best inhibition of clay swelling.

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

Scaling Formulae for the Wellbore Hydraulics Similitude with Drill Pipe Rotation and Eccentricity

2021 ◽  
Author(s):  
Thad Nosar ◽  
Pooya Khodaparast ◽  
Wei Zhang ◽  
Amin Mehrabian
Keyword(s):  
Power Law ◽  
Flow Rate ◽  
Annular Flow ◽  
Rotation Speed ◽  
Drilling Fluid ◽  
Drill Pipe ◽  
Flow Loop ◽  
Annular Flows ◽  
Fluid Rheology ◽  
Pipe Rotation

Abstract Equivalent circulation density of the fluid circulation system in drilling rigs is determined by the frictional pressure losses in the wellbore annulus. Flow loop experiments are commonly used to simulate the annular wellbore hydraulics in the laboratory. However, proper scaling of the experiment design parameters including the drill pipe rotation and eccentricity has been a weak link in the literature. Our study uses the similarity laws and dimensional analysis to obtain a complete set of scaling formulae that would relate the pressure loss gradients of annular flows at the laboratory and wellbore scales while considering the effects of inner pipe rotation and eccentricity. Dimensional analysis is conducted for commonly encountered types of drilling fluid rheology, namely, Newtonian, power-law, and yield power-law. Appropriate dimensionless groups of the involved variables are developed to characterize fluid flow in an eccentric annulus with a rotating inner pipe. Characteristic shear strain rate at the pipe walls is obtained from the characteristic velocity and length scale of the considered annular flow. The relation between lab-scale and wellbore scale variables are obtained by imposing the geometric, kinematic, and dynamic similarities between the laboratory flow loop and wellbore annular flows. The outcomes of the considered scaling scheme is expressed in terms of closed-form formulae that would determine the flow rate and inner pipe rotation speed of the laboratory experiments in terms of the wellbore flow rate and drill pipe rotation speed, as well as other parameters of the problem, in such a way that the resulting Fanning friction factors of the laboratory and wellbore-scale annular flows become identical. Findings suggest that the appropriate value for lab flow rate and pipe rotation speed are linearly related to those of the field condition for all fluid types. The length ratio, density ratio, consistency index ratio, and power index determine the proportionality constant. Attaining complete similarity between the similitude and wellbore-scale annular flow may require the fluid rheology of the lab experiments to be different from the drilling fluid. The expressions of lab flow rate and rotational speed for the yield power-law fluid are identical to those of the power-law fluid case, provided that the yield stress of the lab fluid is constrained to a proper value.

Application of R/S Rheometer in Low Temperature Drilling Fluid Rheology Determination

2012 ◽  
Vol 490-495 ◽  
pp. 3114-3118
Author(s):  
Xiao Ling Jiang ◽  
Zong Ming Lei ◽  
Kai Wei
Keyword(s):  
Shear Stress ◽  
Shear Rate ◽  
Low Temperature ◽  
High Speed ◽  
Drilling Fluid ◽  
Drilling Fluids ◽  
Rheological Parameter ◽  
Fluid Temperature ◽  
Fluid Rheology ◽  
Rotary Viscometer

With six-speed rotary viscometer measuring the rheology of drilling fluid at low temperature, during the high-speed process, the drilling fluid temperature is not constant at low temperature, which leads to the inaccuracy in rheological measurement. When R/S rheometer is used cooperating with constant low-temperature box , the temperature remains stable during the process of determining the drilling fluid rheology under low temperature. The R/S rheometer and the six-speed rotational viscometer are both coaxial rotational viscometers, but they work in different ways and the two cylindrical clearance between them are different.How to make two viscometer determination result can maintain consistent?The experimental results show that, The use of R/S rheometer, with the shear rate for 900s-1 shear stress values instead of six speed rotary viscometer shear rate for 1022s-1 shear stress values.Then use two-point formula to calculate rheological parameters.The R/S rheometer rheological parameter variation with temperature has a good linear relationship,Can better reflect the rheological properties of drilling fluids with low temperature changerule

Internal and external factors effect on the mobile pressure sensors’ performance

Sensor Review ◽  
2016 ◽  
Vol 36 (4) ◽  
pp. 405-413 ◽  
Author(s):  
Semih Dalgin ◽  
Ahmet Özgür Dogru
Keyword(s):  
Pressure Sensor ◽  
Mobile Applications ◽  
Pressure Sensors ◽  
External Factors ◽  
Sensor Data ◽  
High Tech ◽  
Internal Factors ◽  
Content Type ◽  
Internal And External Factors ◽  
Mems Pressure Sensors

Purpose The purpose of this study is to investigate the effect of internal and external factors on the accuracy and consistency of the data provided by mobile-embedded micro-electromechanical system (MEMS) pressure sensors based on smartphones currently in use. Design/methodology/approach For this purpose, sensor type and smartphone model have been regarded as internal factors, whereas temperature, location and usage habits have been considered as external factors. These factors have been investigated by examining data sets provided by sensors from 14 different smartphones. In this context, internal factors have been analyzed by implementing accuracy assessment processes for three different smartphone models, whereas external factors have been evaluated by analyzing the line charts which present timely pressure changes. Findings The study outlined that the sensor data at different sources have different characteristics due to the affecting parameters. Even if the pressure sensors are used under similar circumstances, data of these sensors have inconsistencies because of the sensor drift originated by internal factors. This study concluded that it was not applicable to provide a common correction coefficient for pressure sensor data of each smartphone model. Therefore, relative data (pressure differences) should be taken into consideration rather than absolute data (pressure values) when developing mobile applications using sensor data. Research limitations/implications Results of this study can be used as the guideline for developing mobile applications using MEMS pressure sensors. One of the main finding of this paper is promoting the use of relative data (pressure differences) rather than absolute data (pressure values) when developing mobile applications using smartphone-embedded sensor data. This significant result was proved by examinations applied with in the study and can be applied by future research studies. Originality/value Existing studies mostly evaluate the use of MEMS pressure sensor data obtained from limited number of smartphone models. As each smartphone model has a specific technology, factors affecting the sensor performances should be identified and analyzed precisely in terms of smartphone models for providing extensive results. In this study, five smartphone models were used fractionally. In this context, they were used for examining the common effects of the factors, and detailed accuracy assessments were applied by using two high-tech smartphones in the market.

scholarly journals NEURO-FUZZY SYSTEM FOR DETECTING PD PATIENTS BASED ON EUCLID DISTANCE, FFT, AND PCA

2020 ◽  
Vol 20 (09) ◽  
pp. 2040017
Author(s):  
SEOK-WOO JANG ◽  
SANG-HONG LEE
Keyword(s):  
Pressure Sensor ◽  
Euclidean Distance ◽  
Performance Enhancement ◽  
Principal Component ◽  
Sensor Data ◽  
Second Step ◽  
Neuro Fuzzy ◽  
Euclidean Distances ◽  
Left And Right ◽  
The Individual

This study proposes a method to distinguish between healthy people and Parkinson’s disease patients using sole pressure sensor data, neural network with weighted fuzzy membership (NEWFM), and preprocessing techniques. The preprocessing techniques include fast Fourier transform (FFT), Euclidean distance, and principal component analysis (PCA), to remove noise in the data for performance enhancement. To make the features usable as inputs for NEWFM, the Euclidean distances between the left and right sole pressure sensor data were used at the first step. In the second step, the frequency scales of the Euclidean distances extracted in the first step were divided into individual scales by the FFT using the Hamming method. In the final step, 1–15 dimensions were extracted as the features of NEWFM from the individual scales by the FFT extracted in the second step by the PCA. An accuracy of 75.90% was acquired from the eight dimensions as the inputs of NEWFM.

scholarly journals EPILLOW: A FABRIC-BASED PRESSURE SENSOR ARRAY FOR TETRAPLEGIC PATIENT CALL DETECTION SYSTEM

2021 ◽  
Vol 84 (1) ◽  
pp. 183-192
Author(s):  
Normazlianita Mohamad Alias ◽  
Zakiran Abd Razak ◽  
Munirah Janjori ◽  
Mohd Yazed Ahmad ◽  
Julia Patrick Engkasan ◽  
...  
Keyword(s):  
Signal Processing ◽  
Pressure Sensor ◽  
Essential Role ◽  
Sensor Array ◽  
Detection System ◽  
Pressure Profile ◽  
Sensor Data ◽  
Signal Processing Algorithm ◽  
Lying Down

Call bell systems play an essential role in patient and nurse interaction in hospitals and at homes. However, many hospitalized patients, especially patients with tetraplegia, cannot press a call bell button for assistance due to hand weakness or paralysis from the neck down. This problem has motivated developing a fabric-based multi-array pressure sensor as a call bell garment, named ePillow, that works by detecting the pressure pattern on a pillow surface where the patient is lying down. In this study, off-the-shelf materials were utilized to form: i) a fabric-based multi-array pressure sensor system, ii) an acquisition circuit along with an interface, and iii) a signal processing algorithm to acquire and interpret the sensor data. To ensure the functionality of the proposed ePillow, a color-coded mesh plot was developed to visualize the sensor data. The reliability of the system was tested with two individuals. The pressure profile of the proposed ePillow shows a comparable profile to that of the commercialized pressure sensor. Findings from this case study have demonstrated the ability to map the force on the surface of the pillow and subsequently the location of the force applied with 71% accuracy and 70% sensitivity.  

Streaming Pressure Sensor Data for Cloud Processing

2019 ◽  
Author(s):  
R Bruce Wallace ◽  
Haoyang Liu ◽  
Rafik Goubran ◽  
Martin Bilodeau ◽  
Frank Knoefel
Keyword(s):  
Pressure Sensor ◽  
Sensor Data ◽  
Cloud Processing
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