Effect of Different Qualities of Foam on Fill Particle Transport in Horizontal Well Cleanup Operation Using Coiled Tubing

Khan, Javed Akbar; Pao, William

doi:10.4028/www.scientific.net/amr.903.39

Cited by 12 publications

(7 citation statements)

References 19 publications

(16 reference statements)

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“…In some cases, during well-cleanup operations with coiled tubing, the sticking of the coiled tubing occurs, especially during sand transport form horizontal wellbores [15,16]. A computational fluid dynamics approach has been used to circulate the injection of low quality foam at a high flow rate to successfully clean the wellbore without the sticking of the coiled tubing [17,18]. Moreover, an optimized design of the coiled tubing nozzle has been presented to increase the outlet pressure for higher sand removal from the wellbore to avoid the sticking of the tubing [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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“…32 Moreover, foam is very effective in the wellbore cleanup operation as it can efficiently suspend the sand particles. 33,34 The viscosity of the foam-based hydraulic fracturing fluid depends on the foam quality (the proportion of gas in the mixture), as well as the foam texture or the consistency of bubbles. The foam quality is usually between 95 and 98%, restricting water usage to less than 5% (ultra-low water content).…”

Section: Introductionmentioning

confidence: 99%

“…CO 2 foam-based fluid mitigates fluid loss in the fracturing process and has been shown to demonstrate high proppant carrying capacities in water-sensitive formations . Moreover, foam is very effective in the wellbore cleanup operation as it can efficiently suspend the sand particles. , …”

Section: Introductionmentioning

confidence: 99%

Experimental Assessment of Fracture Initiation and Wettability in Low and High Brittle Shales by CO₂ Foam Fracturing Fluids

et al. 2022

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CO2 foam fracturing fluid has received significant attention because of its versatility in water-sensitive formations. However, the mechanisms of hydraulic fracturing of shale reservoirs by CO2 foam fracking fluid, in terms of fracture initiation and propagation remain unclear. In this paper, an experimental study was conducted to correlate the fluid interaction and fracture propagation in various shale formations having brittleness indices of 0.43, 0.50, and 0.74 to the rock-fracking fluid wettability. The shale wetting state in the presence of the CO2 foam fracturing fluid was determined from contact angle measurements through the Krüss drop shape analyzer (DSA 100), and the hydraulic fracturing was conducted via a tri-axial fracking system. A specific concentration of polyacrylamide (500 ppm) is used as a drag-reducing agent and selected for the hydraulic fracturing experiments. CO2 foam fracturing resulted in low fracture initiation pressure in the shale, having a high brittleness index of 0.74 BIm, and it remains the same at high temperatures. The length and aperture of the hydraulic fracture were then correlated with fracturing fluid–shale contact angles. The Mancos shale is sand-rich (with 21 wt % calcite and 52 wt % quartz), and it remained strongly water-wet on the interaction with the fracking fluid, and the net breakdown pressure was found to be lower in the fracturing tests of the Mancos shale. The Marcellus shale (with 31 wt % calcite and 22 wt % quartz) was intermediately wet (with a contact angle of 93.1°), whereas the Eagle Ford shale with high clay content (77 wt % calcite and 11 wt % quartz) was CO2-wet (with a contact angle of 109°) at 80 °C. Marcellus and Eagle Ford shales demonstrated higher fracture initiation pressures during the fracking experiments than the Mancos shale. The study suggests that fracking fluid–shale wettability has an essential role in the fracking of shale formations and should be considered in optimizing hydraulic fracturing fluid.

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“…In the case of monolayer proppant distribution, the embedment depth and contact stress decrease with the increase in proppant concentration [16]. In the past, machine learning and computational fluid dynamics approaches have been used to explore the well operation and the transport of sand particles by the injection of foam [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Fracture Conductivity in Shale Reservoirs

Khan¹,

Padmanabhan²,

Haq³

2022

Emerging Technologies in Hydraulic Fracturing and Gas Flow Modelling

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Optimum conductivity is essential for hydraulic fracturing due to its significant role in maintaining productivity. Hydraulic fracture networks with required fracture conductivities are decisive for the cost-effective production from unconventional shale reservoirs. Fracture conductivity reduces significantly in shale formations due to the high embedment of proppants. In this research, the mechanical properties of shale samples from Sungai Perlis beds, Terengganu, Malaysia, have been used for computational contact analysis of proppant between fracture surfaces. The finite element code in ANSYS is used to simulate the formation/proppant contact-impact behavior in the fracture surface. In the numerical analysis, a material property of proppant and formation characteristics is introduced based on experimental investigation. The influences of formation load and resulted deformation of formation are calculated by total penetration of proppant. It has been found that the formation stresses on both sides of fractured result in high penetration of proppant in the fracture surfaces, although proppant remains un-deformed.

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Effect of Different Qualities of Foam on Fill Particle Transport in Horizontal Well Cleanup Operation Using Coiled Tubing

Cited by 12 publications

References 19 publications

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Experimental Assessment of Fracture Initiation and Wettability in Low and High Brittle Shales by CO₂ Foam Fracturing Fluids

Hydraulic Fracture Conductivity in Shale Reservoirs

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Effect of Different Qualities of Foam on Fill Particle Transport in Horizontal Well Cleanup Operation Using Coiled Tubing

Cited by 12 publications

References 19 publications

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Experimental Assessment of Fracture Initiation and Wettability in Low and High Brittle Shales by CO2 Foam Fracturing Fluids

Hydraulic Fracture Conductivity in Shale Reservoirs

Contact Info

Product

Resources

About

Experimental Assessment of Fracture Initiation and Wettability in Low and High Brittle Shales by CO₂ Foam Fracturing Fluids