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.

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

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.

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.

Wellbore Shielding Technique Increases Operative Window, Avoiding Formation Instability and Losses, Minimizing NPT, and Optimizing Drilling Operations in the Unconventional Plays

2021 ◽  
Author(s):  
Gaston Lopez ◽  
Gonzalo Vidal ◽  
Claus Hedegaard ◽  
Reinaldo Maldonado
Keyword(s):  
Drilling Fluid ◽  
Drilling Fluids ◽  
Wellbore Instability ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Drilling Parameters ◽  
Vaca Muerta ◽  
Drilling Operations ◽  
Fracture Gradient ◽  
Vaca Muerta Formation

Abstract Losses, wellbore instability, and influxes during drillings operations in unconventional fields result from continuous reactivity to the drilling fluid causing instability in the microfractured limestone of the Quintuco Formation in Argentina. This volatile situation becomes more critical when drilling operations are navigating horizontally through the Vaca Muerta Formation, a bituminous marlstone with a higher density than the Quintuco Formation. Controlling drilling fluids invasion between the communicating microfractures and connecting pores helps to minimize seepage losses, total losses, wellbore fluid influxes, and instabilities, reducing the non-productive time (NPT) caused by these problems during drilling operations. The use of conventional sealants – like calcium carbonate, graphite, asphalt, and other bridging materials – does not guarantee problem-free drilling operations. Also, lost circulation material (LCM) is restricted because the MWD-LWD tools clearances are very narrow in these slim holes. The challenge is to generate a strong and resistant seal separating the drilling fluid and the formation. Using an ultra-low-invasion technology will increase the operative fracture gradient window, avoid fluid invasion to the formation, minimize losses, and stop the cycle of fluid invasion and instability, allowing operations to maintain the designed drilling parameters and objectives safely. The ultra-low-invasion wellbore shielding technology has been applied in various fields, resulting in significantly improved drilling efficiencies compared to offset wells. The operator has benefited from the minimization of drilling fluids costs and optimization in drilling operations, including reducing the volume of oil-based drilling fluids used per well, fewer casing sections, and fewer requirements for cementing intervals to solve lost circulation problems. This paper will discuss the design of the ultra-low-invasion technology in an oil-based drilling fluid, the strategy for determining the technical limits for application, the evaluation of the operative window with an increase in the fracture gradient, the optimized drilling performance, and reduction in costs, including the elimination of NPT caused by wellbore instability.

Automated Real-Time Roadway Asset Inventory using Artificial Intelligence

2020 ◽  
Vol 2674 (11) ◽  
pp. 220-234
Author(s):  
Nima Kargah-Ostadi ◽  
Ammar Waqar ◽  
Adil Hanif
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Asset Management ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Training Dataset ◽  
Traffic Signs ◽  
Asset Inventory

Roadway asset inventory data are essential in making data-driven asset management decisions. Despite significant advances in automated data processing, the current state of the practice is semi-automated. This paper demonstrates integration of the state-of-the-art artificial intelligence technologies within a practical framework for automated real-time identification of traffic signs from roadway images. The framework deploys one of the very latest machine learning algorithms on a cutting-edge plug-and-play device for superior effectiveness, efficiency, and reliability. The proposed platform provides an offline system onboard the survey vehicle, that runs a lightweight and speedy deep neural network on each collected roadway image and identifies traffic signs in real-time. Integration of these advanced technologies minimizes the need for subjective and time-consuming human interventions, thereby enhancing the repeatability and cost-effectiveness of the asset inventory process. The proposed framework is demonstrated using a real-world image dataset. Appropriate pre-processing techniques were employed to alleviate limitations in the training dataset. A deep learning algorithm was trained for detection, classification, and localization of traffic signs from roadway imagery. The success metrics based on this demonstration indicate that the algorithm was effective in identifying traffic signs with high accuracy on a test dataset that was not used for model development. Additionally, the algorithm exhibited this high accuracy consistently among the different considered sign categories. Moreover, the algorithm was repeatable among multiple runs and reproducible across different locations. Above all, the real-time processing capability of the proposed solution reduces the time between data collection and delivery, which enhances the data-driven decision-making process.

scholarly journals NETL Extreme Drilling Laboratory Studies High Pressure High Temperature Drilling Phenomena

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
K. David Lyons ◽  
Simone Honeygan ◽  
Thomas Mroz
Keyword(s):  
Physical Simulation ◽  
Drilling Fluid ◽  
Fluid Pressure ◽  
Rock Properties ◽  
Drilling Fluids ◽  
Laboratory Studies ◽  
Drilling Parameters ◽  
High Pressure High Temperature ◽  
Fluid Properties ◽  
Weight On Bit

The U.S. Department of Energy’s National Energy Technology Laboratory (NETL) established the Extreme Drilling Laboratory to engineer effective and efficient drilling technologies viable at depths greater than 20,000 ft. This paper details the challenges of ultradeep drilling, documents reports of decreased drilling rates as a result of increasing fluid pressure and temperature, and describes NETL’s research and development activities. NETL is invested in laboratory-scale physical simulation. Its physical simulator will have capability of circulating drilling fluids at 30,000 psi and 480°F around a single drill cutter. This simulator is not yet operational; therefore, the results will be limited to the identification of leading hypotheses of drilling phenomena and NETL’s test plans to validate or refute such theories. Of particular interest to the Extreme Drilling Laboratory’s studies are the combinatorial effects of drilling fluid pressure, drilling fluid properties, rock properties, pore pressure, and drilling parameters, such as cutter rotational speed, weight on bit, and hydraulics associated with drilling fluid introduction to the rock-cutter interface. A detailed discussion of how each variable is controlled in a laboratory setting will be part of the conference paper and presentation.

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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