Making Real Time Fluid Decisions with Real Time Fluid Data at the Rig Site: Results of Automated Drilling Fluid Measurement Field Trials

2010 ◽  
Author(s):  
Shawn Broussard ◽  
Peter Gonzalez ◽  
Robert Jerome Murphy ◽  
Chris Marvel
2021 ◽  
Vol 73 (06) ◽  
pp. 31-33
Author(s):  
Blake Wright

As industry buzzwords go, “automation” has spent its time in oilfield vernacular climbing the ranks of widely used terms. It now resides as one of the go-to designations for signs of advancement in any number of disciplines. Its use has been tied most frequently with drilling operations as contractors look to keep employees out of harm’s way via a robotic take-over of most motion-intensive jobs on the rig’s drill floor—basically anything that grips, clamps, or spins. More recently, the term has moved away from the drill floor and into other well construction operations allowing for things such as remote, real-time measurements without the need for boots on the ground. For areas like west Texas and the Permian Basin shales, having the option for remote readouts and a component of automation that can allow for corrective actions should the need arise can go a long way in terms of safety and efficiency gains as well as better manpower application. Unsurprisingly, the area has become a solid testing ground for new, expanding efforts in automation. With dreams of new drilling-fluid-monitoring automation, Eric van Oort, a professor at The University of Texas at Austin and former Shell research scientist, and select students came up with a new way to automatically measure mud parameters such as viscosity without the use of a traditional viscometer. “The fact that we still use manual measurements, some of them now 90 years old, is quite puzzling in this day and age,” van Oort said. “The Marsh funnel, for instance, was introduced in the 1930s, and other mud tests go back to the 1950s and 1960s. These API measurements have served us well, but the question is, can you do something more now with modern measurement techniques and sensors? So, I started working on new ways of measuring the viscosity and density, and then later fluid loss and even solids and salinity in muds. That proved to be all very successful and promising.” Construction of a mud skid to house the equipment and sensors needed to conduct these tests in real time was the next step in the evolution of van Oort’s concept. That initial skid was a cannibalized and reworked version of a unit that was employed on Shell’s Rig 1, which the supermajor built for its in-house rig-automation research based in Pennsylvania. This early mud skid, considered the prototype of van Oort’s design, was abandoned before it was properly tested. “We generated quite a bit of IP [intellectual property], my students and I at UT,” he said. “The Shell skid hadn’t seen a significant amount of service, and it had some nice components that we could reuse. We took that skid apart and reconfigured it and put it out in the field with Pioneer Natural Resources for a set of field trials in the Permian. Those went well.” The field trial results were shared in a paper presented at the 2019 Unconventional Resources Technology Conference (URTEC 2019-964). The paper concluded that the pipe viscometer employed by the skid allows for the characterization of additional rheology parameters, which cannot be obtained with Couette-type viscometers, such as the critical Reynolds number, characterizing the transition from laminar into turbulent flow, and the friction factor in the turbulent flow regime.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3592
Author(s):  
Naipeng Liu ◽  
Di Zhang ◽  
Hui Gao ◽  
Yule Hu ◽  
Longchen Duan

The accurate and frequent measurement of the drilling fluid’s rheological properties is essential for proper hydraulic management. It is also important for intelligent drilling, providing drilling fluid data to establish the optimization model of the rate of penetration. Appropriate drilling fluid properties can improve drilling efficiency and prevent accidents. However, the drilling fluid properties are mainly measured in the laboratory. This hinders the real-time optimization of drilling fluid performance and the decision-making process. If the drilling fluid’s properties cannot be detected and the decision-making process does not respond in time, the rate of penetration will slow, potentially causing accidents and serious economic losses. Therefore, it is important to measure the drilling fluid’s properties for drilling engineering in real time. This paper summarizes the real-time measurement methods for rheological properties. The main methods include the following four types: an online rotational Couette viscometer, pipe viscometer, mathematical and physical model or artificial intelligence model based on a Marsh funnel, and acoustic technology. This paper elaborates on the principle, advantages, limitations, and usage of each method. It prospects the real-time measurement of drilling fluid rheological properties and promotes the development of the real-time measurement of drilling rheological properties.


Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5857
Author(s):  
Brandy J. Johnson ◽  
Anthony P. Malanoski ◽  
Jeffrey S. Erickson

This review describes an ongoing effort intended to develop wireless sensor networks for real-time monitoring of airborne targets across a broad area. The goal is to apply the spectrophotometric characteristics of porphyrins and metalloporphyrins in a colorimetric array for detection and discrimination of changes in the chemical composition of environmental air samples. The work includes hardware, software, and firmware design as well as development of algorithms for identification of event occurrence and discrimination of targets. Here, we describe the prototype devices and algorithms related to this effort as well as work directed at selection of indicator arrays for use with the system. Finally, we review the field trials completed with the prototype devices and discuss the outlook for further development.


2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  

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.


2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali ◽  
Mahmoud Abughaban ◽  
Abdulazeez Abdulraheem

Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R). All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R=0.99 and RMSE=0.05. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.


2018 ◽  
Vol 269 ◽  
pp. 138-141 ◽  
Author(s):  
Sanguo Li ◽  
Lizhi Xiao ◽  
Xin Li ◽  
Zhizhan Wang

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Tengku Ezharuddin Tengku Bidin ◽  
...  

Abstract The restriction or inability of the drill string to reciprocate or rotate while in the borehole is commonly known as a stuck pipe. This event is typically accompanied by constraints in drilling fluid flow, except for differential sticking. The stuck pipe can manifest based on three different mechanisms, i.e. pack-off, differential sticking, and wellbore geometry. Despite its infrequent occurrence, non-productive time (NPT) events have a massive cost impact. Nevertheless, stuck pipe incidents can be evaded with proper identification of its unique symptoms which allows an early intervention and remediation action. Over the decades, multiple analytical studies have been attempted to predict stuck pipe occurrences. The latest venture into this drilling operational challenge now utilizes Machine Learning (ML) algorithms in forecasting stuck pipe risk. An ML solution namely, Wells Augmented Stuck Pipe Indicator (WASP), is developed to tackle this specific challenge. The solution leverages on real-time drilling database and supplementary engineering design information to estimate proxy drilling parameters which provide active and impartial pattern recognition of prospective stuck pipe events. The solution is built to assist Wells Real Time Centre (WRTC) personnel in proactively providing a holistic perspective in anticipating potential anomalies and recommending remedial countermeasures before incidents happen. Several case studies are outlined to exhibit the impact of WASP in real-time drilling operation monitoring and intervention where WASP is capable to identify stuck pipe symptoms a few hours earlier and provide warnings for stuck pipe avoidance. The presented case studies were run on various live wells where restrictions are predicted stands ahead of the incidents. Warnings and alarms were generated, allowing further analysis by the personnel to verify and assess the situation before delivering a precautionary procedure to the rig site. The implementation of the WASP will reduce analysis time and provide timely prescriptive action in the proactive real-time drilling operation monitoring and intervention hub, subsequently creating value through cost containment and operational efficiency.


2021 ◽  
Author(s):  
Kriti Singh ◽  
Sai Yalamarty ◽  
Curtis Cheatham ◽  
Khoa Tran ◽  
Greg McDonald

Abstract This paper is a follow up to the URTeC (2019-343) publication where the training of a Machine Learning (ML) model to predict rate of penetration (ROP) is described. The ML model gathers recent drilling parameters and approximates drilling conditions downhole to predict ROP. In real time, the model is run through an optimization sweep by adjusting parameters which can be controlled by the driller. The optimal drilling parameters and modeled ROP are then displayed for the driller to utilize. The ML model was successfully deployed and tested in real time in collaboration with leading shale operators in the Permian Basin. The testing phase was split in two parts, preliminary field tests and trials of the end-product. The key learnings from preliminary field tests were used to develop an integrated driller's dashboard with optimal drilling parameters recommendations and situational awareness tools for high dysfunction and procedural compliance which was used for designed trials. The results of field trials are discussed where subject well ROP was improved between 19-33% when comparing against observation/control footage. The overall ROP on subject wells was also compared against offset wells with similar target formations, BHAs, and wellbore trajectories. In those comparisons against qualified offsets, ROP was improved by as little as 5% and as much as 33%. In addition to comparing ROP performance, results from post-run data analysis are also presented. Detailed drilling data analytics were performed to check if using the recommendations during the trial caused any detrimental effects such as divergence in directional trends or high lateral or axial vibrations. The results from this analysis indicate that the measured downhole axial and lateral vibrations were in the safe zone. Also, no significant deviations in rotary trends were observed.


2016 ◽  
Vol 17 (1) ◽  
pp. 1-5 ◽  
Author(s):  
S. J. Anderson ◽  
H. E. Simmons ◽  
R. D. French-Monar ◽  
G. P. Munkvold

A real-time PCR assay was used to compare seedling infection by Sphacelotheca reiliana, the causal agent of head smut, among five inbred genotypes representing low, moderate, and high susceptibility to the disease. Seeds were coated with teliospores and planted in autoclaved field soil in a growth chamber. Incidence of seedling infection at growth stage V3 differed between an inbred genotype of low susceptibility and those of moderate and high susceptibility, but did not differ between the high and moderately susceptible groups (P < 0.05). The real-time PCR assay was also used to compare infection status at early and late vegetative stages with observable symptoms in the field. We detected infection via real-time PCR in maize at both growth stages during field trials conducted in Texas and California but observed no disease symptoms (smutted ears or tassels). Notably, the fungus was present in up to 31% of the ear shoots in plots without disease symptoms. The real-time assay can be a useful tool for screening seedling-stage host resistance, and for better understanding the progress of infection in different maize genotypes. The field data suggest that asymptomatic infection is much more common than previously thought, and may have important implications for the epidemiology of this fungus under diverse plant resistance and growing conditions. Accepted for publication 11 December 2015. Published 5 January 2016.


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