Case Studies for the Successful Deployment of Wells Augmented Stuck Pipe Indicator in Wells Real Time Centre

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Tengku Ezharuddin Tengku Bidin ◽  
...  
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Drilling Fluid ◽  
Drill String ◽  
Drilling Operation ◽  
Cost Impact ◽  
Drilling Parameters ◽  
The Impact ◽  
Productive Time ◽  
Specific Challenge

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.

On the Importance of the Coupling Between Transient Mechanical, Hydraulic and Thermal Effects for the Modelling of Real-Time Drilling Operations

2019 ◽  
Author(s):  
Erik Wolden Dvergsnes ◽  
Eric Cayeux
Keyword(s):  
Real Time ◽  
Drilling Fluid ◽  
Mass Density ◽  
Thermal Conditions ◽  
Time Discretization ◽  
Drill String ◽  
Drilling Process ◽  
Drilling Operation ◽  
Improved Performance ◽  
Transient Models

Abstract Because of the increased importance for the drilling industry to deliver drilling automation solutions, model-based applications for the analysis and control of the drilling process, have become an attractive approach towards improved performance and increased safety. A critical characteristic for such applications is its ability to perform accurate simulations of the drilling operation in real-time, based on a detailed description of the wellbore. In a real-time context, the boundary conditions of the drilling system are seldom constant, therefore reinforcing the importance of utilizing transient models of the drilling process instead of steady state ones. Typical domains that require modelling are related to the mechanical, hydraulic and heat transfer aspects of a drilling operation. The time constants of the force-, momentum-, mass- and energy-conservation equations are sufficiently different to allow for solving each of these equations with different time discretization schemes. Yet, side effects influence the results from each other’s and therefore a time coupling shall nevertheless be accounted for. For instance, for a drilling operation conducted on a floater, the heave induced movement at the top of the string propagates along the drill-string, therefore causing a displacement that induces swab and surge pressure variations, which themselves generate counter-acting forces on the drill-string. In such conditions, both the mechanical and hydraulic frictions generate heat that changes the in situ thermal conditions and therefore the drilling fluid mass density and its rheological behavior. Consequently, heat exchange caused by the drill-string and fluid movements also influences the hydraulic response of the system. Furthermore, thermal expansion will also apply to the drill-string. In this paper, we discuss recent advances related to the coupling between transient mechanical, hydraulic and thermal models, where a key criterion is that the combined drilling model shall be capable of running in real-time on a standard computer. Incorporating these transient models is considered a necessary step towards improved accuracy of simulations, especially on floaters, where heave effects become important. We illustrate various effects by presenting and discussing several simulations results in detail.

A Pandemic of Violence? The Impact of COVID-19 on Conflict

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Sara M.T. Polo
Keyword(s):  
Spatial Distribution ◽  
Real Time ◽  
Case Studies ◽  
Armed Conflict ◽  
Event Data ◽  
Social Unrest ◽  
Johns Hopkins University ◽  
The World ◽  
Political Advantage ◽  
The Impact

AbstractThis article examines the impact and repercussions of the COVID-19 pandemic on patterns of armed conflict around the world. It argues that there are two main ways in which the pandemic is likely to fuel, rather than mitigate, conflict and engender further violence in conflict-prone countries: (1) the exacerbating effect of COVID-19 on the underlying root causes of conflict and (2) the exploitation of the crisis by governments and non-state actors who have used the coronavirus to gain political advantage and territorial control. The article uses data collected in real-time by the Armed Conflict Location & Event Data Project (ACLED) and the Johns Hopkins University to illustrate the unfolding and spatial distribution of conflict events before and during the pandemic and combine this with three brief case studies of Afghanistan, Nigeria, and Libya. Descriptive evidence shows how levels of violence have remained unabated or even escalated during the first five months of the pandemic and how COVID-19-related social unrest has spread beyond conflict-affected countries.

IIOT and Real Time Data Analytics - Maximizing the Impact on Safety and Productivity

2021 ◽  
Author(s):  
Ryan Daher ◽  
Nesma Aldash
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Data Analytics ◽  
Contact Tracing ◽  
Time Data ◽  
Seamless Integration ◽  
Location Services ◽  
Industrial Internet ◽  
Potential Applications ◽  
The Impact

Abstract With the global push towards Industry 4.0, a number of leading companies and organizations have invested heavily in Industrial Internet of Things (IIOT's) and acquired a massive amount of data. But data without proper analysis that converts it into actionable insights is just more information. With the advancement of Data analytics, machine learning, artificial intelligence, numerous methods can be used to better extract value out of the amassed data from various IIOTs and leverage the analysis to better make decisions impacting efficiency, productivity, optimization and safety. This paper focuses on two case studies- one from upstream and one from downstream using RTLS (Real Time Location Services). Two types of challenges were present: the first one being the identification of the location of all personnel on site in case of emergency and ensuring that all have mustered in a timely fashion hence reducing the time to muster and lessening the risks of Leaving someone behind. The second challenge being the identification of personnel and various contractors, the time they entered in productive or nonproductive areas and time it took to complete various tasks within their crafts while on the job hence accounting for efficiency, productivity and cost reduction. In both case studies, advanced analytics were used, and data collection issues were encountered highlighting the need for further and seamless integration between data, analytics and intelligence is needed. Achievements from both cases were visible increase in productivity and efficiency along with the heightened safety awareness hence lowering the overall risk and liability of the operation. Novel/Additive Information: The results presented from both studies have highlighted other potential applications of the IIOT and its related analytics. Pertinent to COVID-19, new application of such approach was tested in contact tracing identifying workers who could have tested positive and tracing back to personnel that have been in close proximity and contact therefore reducing the spread of COVID. Other application of the IIOT and its related analytics has also been tested in crane, forklift and heavy machinery proximity alert reducing the risk of accidents.

scholarly journals Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali ◽  
Mahmoud Abughaban ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Drilling Fluid ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Functional Networks ◽  
Lost Circulation ◽  
Drilling Parameters ◽  
High Pressure High Temperature ◽  
Drilling Cost

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.

scholarly journals Comparative Analysis of the Effect of Barite and Hematite on the Rheology of Water-Based Drilling Mud

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Osei H
Keyword(s):  
Rheological Properties ◽  
Drilling Fluid ◽  
Attrition Rate ◽  
Drilling Mud ◽  
Cleaning Efficiency ◽  
Drilling Operation ◽  
Water Based ◽  
Petroleum Resources ◽  
Drilling Muds ◽  
The Impact

High demand for oil and gas has led to exploration of more petroleum resources even at remote areas. The petroleum resources are found in deeper subsurface formations and drilling into such formations requires a well-designed drilling mud with suitable rheological properties in order to avoid or reduce associated drilling problems. This is because rheological properties of drilling muds have considerable effect on the drilling operation and cleaning of the wellbore. Mud engineers therefore use mud additives to influence the properties and functions of the drilling fluid to obtain the desired drilling mud properties especially rheological properties. This study investigated and compared the impact of barite and hematite as weighting agents for water-based drilling muds and their influence on the rheology. Water-based muds of different concentrations of weighting agents (5%, 10%, 15% and 20% of the total weight of the drilling mud) were prepared and their rheological properties determined at an ambient temperature of 24ᵒC to check their impact on drilling operation. The results found hematite to produce higher mud density, plastic viscosity, gel strength and yield point when compared to barite at the same weighting concentrations. The higher performance of the hematite-based muds might be attributed to it having higher specific gravity, better particle distribution and lower particle attrition rate and more importantly being free from contaminants. The water-based muds with hematite will therefore be more promising drilling muds with higher drilling and hole cleaning efficiency than those having barite.

Leveraging on Machine Learning Solution for Pioneering Wells Augmented Stuck Pipe Indicator in Real Time Centre

2021 ◽  
Author(s):  
Mohamad Hazwan Yusoff ◽  
Meor Muhammad Hakeem Meor Hashim ◽  
Muhammad Hadi Hamzah ◽  
Muhammad Faris Arriffin ◽  
Azlan Mohamad
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection System ◽  
Automation System ◽  
Learning Technology ◽  
Drilling Parameters ◽  
Bottom Hole Assembly ◽  
Signature Pattern ◽  
Monitoring Software ◽  
Productive Time

Abstract Stuck pipe incidents remain as one of the major problems in the drilling industry. The incidents will lead to expensive loss time in daily spread cost, bottom hole assembly cost, sidetracking cost as well as fishing cost. The Wells Augmented Stuck Pipe (WASP) Indicator, a state-of-the-art machine learning technology that seamlessly integrates with PETRONAS existing technologies, is introduced as the stuck pipe prevention detection system for the company. Historical real-time drilling data and stuck pipe incidents reports between 2007 and 2019 are used for the development of machine learning models. The models utilize key drilling parameters such as hookload and equivalent circulating density (ECD) to predict and analyze trends to detect any signature pattern anomalies for various stuck pipe events. The prediction and alarm are displayed in real-time monitoring software to trigger the operation team for prompt intervention. The WASP solution has demonstrated proven outcomes using historical and live well with high confidence in detecting stuck pipe incidents due to differential sticking, hole cleaning, and wellbore geometry. The WASP Indicator is envisaged to provide the company with cutting edge advantages in the industry. It is expected that the system will reduce the identification period and improve the reaction time of the monitoring specialists in recognizing the stuck pipe symptoms and highlighting potential incidents. The system is also bringing value to the company via non-productive time (NPT) cost avoidance and identification of early onset of various stuck pipe events based on distinct mechanisms. With the system, the existing portfolio value can be enhanced via setting dynamic trends and models into historical experiences context. The WASP Indicator is aspired to be the forefront innovation that will leap through the norm and lead the region in a greater plan of drilling automation system.

Real-Time Prediction of Mud Motor Failure Using Surface Sensor Data Features and Trends

2021 ◽  
Author(s):  
Tesleem Lawal ◽  
Pradeepkumar Ashok ◽  
Eric van Oort ◽  
Dandan Zheng ◽  
Matthew Isbell
Keyword(s):  
Real Time ◽  
Cost Optimization ◽  
Damage Index ◽  
Sensor Data ◽  
Drilling Parameters ◽  
Wear And Tear ◽  
Drilling Operations ◽  
Downhole Measurements ◽  
The Impact ◽  
Motor Failure

AbstractMud motor failure is a significant contributor to non-productive time in lower-cost land drilling operations, e.g. in North America. Typically, motor failure prevention methodologies range from re-designing or performing sophisticated analytical modeling of the motor power section, to modeling motor performance using high-frequency downhole measurements. In this paper, we present data analytics methods to detect and predict motor failures ahead of time using primarily surface drilling measurements.We studied critical drilling and non-drilling events as applicable to motor failure. The impacts of mud motor stalls and drill-off times were investigated during on-bottom drilling. For the off-bottom analysis, the impact of variations in connection practices (pick up practices, time spent backreaming, and time spent exposing the tools to damaging vibrations) was investigated. The relative importance of the various features found to be relevant was calculated and incorporated into a real-time mud motor damage index.A historical drilling dataset, consisting of surface data collected from 45 motor runs in lateral hole sections of unconventional shale wells drilled in early to mid-2019, was used in this study. These motor runs contained a mix of failure and non-failure cases. The model was found to accurately predict motor failure due to motor wear and tear. Generally, the higher the magnitude of the impact stalls experienced by the mud motor, the greater the probability of eventual failure. Variations in connection practices were found not to be a major wear-and-tear factor. However, it was found that connection practices varied significantly and were often driller-dependent.The overall result shows that simple surface drilling parameters can be used to predict mud motor failure. Hence, the value derived from surface sensor information for mud motor management can be maximized without the need to run more costly downhole sensors. In addition to this cost optimization, drillers can now monitor motor degradation in real-time using the new mud motor index described here.

Design and Application of Diamond Bit Water Passage System, Seafloor Drill Parameters

2021 ◽  
Author(s):  
Jialiang Wang ◽  
Dilei Qian ◽  
Yang Sun ◽  
Fenfei Peng
Keyword(s):  
Flow Field ◽  
Drilling Fluid ◽  
Optimization Theory ◽  
Parameter Design ◽  
Projection Area ◽  
Drilling Parameters ◽  
Diamond Bit ◽  
The Impact ◽  
Water Passage ◽  
Hole Bottom

Abstract The operation and formation characteristics of a seafloor drill are utilized to design a water passage system for bottom-jetting diamond bits based on the multi-objective optimization theory. Fluid dynamics theory and the effects of bit rotation on the flow field at the hole bottom are used to analyze the impact of structural and drilling parameters of the HQ-size bit on the flow field of the waterway system. Considering the effect of the grinding length ratio of the bit on the lopsided wear of the inner and outer diameters, the water passage system parameter design and maximum projection area of the cutting tooth are effective optimization goals to improve the normal service life of the bit. The flow field of the drilling fluid at the hole bottom becomes more turbulent and the efficiency of carrying cuttings return decreases as the waterway height of the bit increases. The optimal bit rotation speed is 250–400 rpm. When drilling into conventional formations, the pump displacement should be controlled within the range of 50–80 L/min. When drilling into sediment formations, the pump displacement should be controlled within the range of 50–65 L/min. An on-site drilling test verified the rationality of the bit water passage system design and corresponding drilling parameters.

scholarly journals Design of Diamond Bits Water Passage System and Simulation of Bottom Hole Fluid Are Applied to Seafloor Drill

2021 ◽  
Vol 9 (10) ◽  
pp. 1100
Author(s):  
Jialiang Wang ◽  
Dilei Qian ◽  
Yang Sun ◽  
Fenfei Peng
Keyword(s):  
Flow Field ◽  
Drilling Fluid ◽  
Optimization Theory ◽  
Projection Area ◽  
Bottom Hole ◽  
Drilling Parameters ◽  
Sample Disturbance ◽  
The Impact ◽  
Water Passage ◽  
Hole Bottom

The performance of the diamond bit directly affects the drilling efficiency of the seafloor drill. The drill bits used in land drilling are prone to abnormal wear, low coring efficiency, and large sample disturbance in marine exploration. At first, in this paper, the operation and formation characteristics of a seafloor drill are utilized to design a water passage system for bottom-jetting diamond bits based on the multi-objective optimization theory. Additionally, then, fluid dynamics theory and the effects of bit rotation on the flow field at the hole bottom were used to analyze the impact of structural and drilling parameters of the HQ-size bit on the flow field of the waterway system. The linear regression equation of the influence of drilling parameters on the bottom hole velocity field and pressure field is obtained. Finally, a field drilling test of the drill bit was carried out. Considering the effect of the grinding length ratio of the bit on the lopsided wear of the inner and outer diameters, the water passage system parameter design and maximum projection area of the cutting tooth are effective optimization goals to improve the normal service life of the bit. The flow field of the drilling fluid at the hole bottom becomes more turbulent and the efficiency of the carrying cuttings return decreases as the waterway height of the bit increases. The optimal bit rotation speed is 250–400 rpm. When drilling into conventional formations, the pump displacement should be controlled within the range of 50–80 L/min. When drilling into sediment formations, the pump displacement should be controlled within the range of 50–65 L/min. An on-site drilling test verified the rationality of the bit water passage system. This work may enrich the existing theories and designs of the water passage system.

Drilling Parameters Optimization Using an Innovative Artificial Intelligence Model

2021 ◽  
Vol 143 (5) ◽  
Author(s):  
Rahman Ashena ◽  
Minou Rabiei ◽  
Vamegh Rasouli ◽  
Amir H. Mohammadi ◽  
Siamak Mishani
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Critical Factor ◽  
Drill String ◽  
Pattern Search ◽  
Parameters Optimization ◽  
Ann Model ◽  
Drilling Performance ◽  
Drilling Parameters ◽  
Drilling System

Abstract Proper selection of the drilling parameters and dynamic behavior is a critical factor in improving drilling performance and efficiency. Therefore, the development of an efficient artificial intelligence (AI) method to predict the appropriate control parameters is critical for drilling optimization. The AI approach presented in this paper uses the power of optimized artificial neural networks (ANNs) to model the behavior of the non-linear, multi-input/output drilling system. The optimization of the model was achieved by optimizing the controllers (combined genetic algorithm (GA) and pattern search (PS)) to reach the global optima, which also provides the drilling planning team with a quantified recommendation on the appropriate optimal drilling parameters. The optimized ANN model used drilling parameters data recorded real-time from drilling practices in different lithological units. Representative portions of the data sets were utilized in training, testing, and validation of the model. The results of the analysis have demonstrated the AI method to be a promising approach for simulation and prediction of the behavior of the complex multi-parameter drilling system. This method is a powerful alternative to traditional analytic or real-time manipulation of the drilling parameters for mitigation of drill string vibrations and invisible lost time (ILT). The utilization can be extended to the field of drilling control and optimization, which can lead to a great contribution of 73% in reduction of the drilling time.

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