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.

Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Yanfang Wang ◽  
Saeed Salehi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Field Data ◽  
Ann Model ◽  
Time Data ◽  
Neural Network Approach ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Artificial Neural Network Ann ◽  
Pump Pressure

Real-time drilling optimization improves drilling performance by providing early warnings in operation Mud hydraulics is a key aspect of drilling that can be optimized by access to real-time data. Different from the investigated references, reliable prediction of pump pressure provides an early warning of circulation problems, washout, lost circulation, underground blowout, and kicks. This will help the driller to make necessary corrections to mitigate potential problems. In this study, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab. Following the determination of the optimum model, the sensitivity analysis of input parameters on the created model was investigated by using forward regression method. Next, the remaining data from the selected well samples was applied for simulation to verify the quality of the developed model. The novelty is this paper is validation of computer models with actual field data collected from an operator in LA. The simulation result was promising as compared with collected field data. This model can accurately predict pump pressure versus depth in analogous formations. The result of this work shows the potential of the approach developed in this work based on NN models for predicting real-time drilling hydraulics.

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.

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.

Pushing the Limits in Offshore Mexico

2021 ◽  
Author(s):  
Hector Hugo Vizcarra Marin ◽  
Alex Ngan ◽  
Roberto Pineda ◽  
Juan Carlos Gomez ◽  
Jose Antonio Becerra
Keyword(s):  
Gulf Of Mexico ◽  
Real Time ◽  
Lessons Learned ◽  
Clear Understanding ◽  
Directional Drilling ◽  
Case Histories ◽  
Control Plan ◽  
Drilling Performance ◽  
Drilling Parameters ◽  
Drilling Engineering

Abstract Given the increased demands on the production of hydrocarbons and cost-effectiveness for the Operator's development wells, the industry is challenged to continually explore new technology and methodology to improve drilling performance and operational efficiency. In this paper, two recent case histories showcase the technology, drilling engineering, and real-time optimization that resulted in record drilling times. The wells are located on shallow water in the Gulf of Mexico, with numerous drilling challenges, which typically resulted in significant Non-Productive Time (NPT). Through close collaboration with the Operator, early planning with a clear understanding of offset wells challenges, well plan that minimize drilling in the Upper Cretaceous "Brecha" Formation were formulated. The well plan was also designed to reduce the risk of stuck pipe while meeting the requirements to penetrate the geological targets laterally to increase the area of contact in the reservoir section. This project encapsulates the successful application of the latest Push-the-Bit Rotary Steerable System (RSS) with borehole enlargement technology through a proven drilling engineering process to optimize the drilling bottomhole assembly, bit selection, drilling parameters, and real-time monitoring & optimization The records drilling times in the two case histories can be replicated and further improved. A list of lessons learned and recommendations for the future wells are discussed. These include the well trajectory planning, directional drilling BHA optimization, directional control plan, drilling parameters to optimize hole cleaning, and downhole shocks & vibrations management during drilling and underreaming operation to increase the drilling performance ultimately. Also, it includes a proposed drilling blueprint to continually push the limit of incremental drilling performance through the use of RSS with hydraulics drilling reamers through the Jurassic-age formations in shallow waters, Gulf of Mexico.

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.

Evaluation of Derived Controllable Variables for Predicting Rop Using Artificial Intelligence in Autonomous Downhole Rotary Drilling System

2021 ◽  
Author(s):  
Kingsley Williams Amadi ◽  
Ibiye Iyalla ◽  
Yang Liu ◽  
Mortadha Alsaba ◽  
Durdica Kuten
Keyword(s):  
Real Time ◽  
Specific Energy ◽  
Penetration Rate ◽  
Drilling Process ◽  
Stick Slip ◽  
Conservation Of Energy ◽  
Rate Of Penetration ◽  
Mechanical Specific Energy ◽  
Drilling Parameters ◽  
Drilling System

Abstract Fossil fuel energy dominate the world energy mix and plays a fundamental role in our economy and lifestyle. Drilling of wellbore is the only proven method to extract the hydrocarbon reserves, an operation which is both highly hazardous and capital intensive. To optimize the drilling operations, developing a high fidelity autonomous downhole drilling system that is self-optimizing using real-time drilling parameters and able to precisely predict the optimal rate of penetration is essential. Optimizing the input parameters; surface weight on bit (WOB), and rotary speed (RPM) which in turns improves drilling performance and reduces well delivery cost is not trivial due to the complexity of the non-linear bit-rock interactions and changing formation characteristics. However, application of derived variables shows potential to predict rate of penetration and determine the most influential parameters in a drilling process. In this study the use of derived controllable variables calculated from the drilling inputs parameters were evaluated for potential applicability in predicting penetration rate in autonomous downhole drilling system using the artificial neutral network and compared with predictions of actual input drilling parameters; (WOB, RPM). First, a detailed analysis of actual rock drilling data was performed and applied in understanding the relationship between these derived variables and penetration rate enabling the identification of patterns which predicts the occurrence of phenomena that affects the drilling process. Second, the physical law of conservation of energy using drilling mechanical specific energy (DMSE) defined as energy required to remove a unit volume of rock was applied to measure the efficiency of input energy in the drilling system, in combination with penetration rate per unit revolution and penetration rate per unit weight applied (feed thrust) are used to effective predict optimum penetration rate, enabling an adaptive strategize which optimize drilling rate whilst suppressing stick-slip. The derived controllable variable included mechanical specific energy, depth of cut and feed thrust are calculated from the real- time drilling parameters. Artificial Neutral Networks (ANNs) was used to predict ROP using both input drilling parameters (WOB, RPM) and derived controllable variables (MSE, FET) using same network functionality and model results compared. Results showed that derived controllable variable gave higher prediction accuracy when compared with the model performance assessment criteria commonly used in engineering analysis including the correlation coefficient (R2) and root mean square error (RMSE). The key contribution of this study when compared to the previous researches is that it introduced the concept of derived controllable variables with established relationship with both ROP and stick-slip which has an advantage of optimizing the drilling parameters by predicting optimal penetration rate at reduced stick-slip which is essential in achieving an autonomous drilling system. :

Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Optimal Model ◽  
Time Data ◽  
Fracture Pressure ◽  
Drilling Parameters ◽  
Drilling Operations

Abstract Several correlations are available to determine the fracture pressure, a vital property of a well, which is essential in the design of the drilling operations and preventing problems. Some of these correlations are based on the rock and formation characteristics, and others are based on log data. In this study, five artificial intelligence (AI) techniques predicting fracture pressure were developed and compared with the existing empirical correlations to select the optimal model. Real-time data of surface drilling parameters from one well were obtained using real-time drilling sensors. The five employed methods of AI are functional networks (FN), artificial neural networks (ANN), support vector machine (SVM), radial basis function (RBF), and fuzzy logic (FL). More than 3990 datasets were used to build the five AI models by dividing the data into training and testing sets. A comparison between the results of the five AI techniques and the empirical fracture correlations, such as the Eaton model, Matthews and Kelly model, and Pennebaker model, was also performed. The results reveal that AI techniques outperform the three fracture pressure correlations based on their high accuracy, represented by the low average absolute percentage error (AAPE) and a high coefficient of determination (R2). Compared with empirical models, the AI techniques have the advantage of requiring less data, only surface drilling parameters, which can be conveniently obtained from any well. Additionally, a new fracture pressure correlation was developed based on ANN, which predicts the fracture pressure with high precision (R2 = 0.99 and AAPE = 0.094%).

scholarly journals Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3506 ◽  
Author(s):  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Support Vector ◽  
Empirical Correlations ◽  
Validation Data ◽  
Inference System ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Svm Model ◽  
Data Points

Rate of penetration (ROP) is defined as the amount of removed rock per unit area per unit time. It is affected by several factors which are inseparable. Current established models for determining the ROP include the basic mathematical and physics equations, as well as the use of empirical correlations. Given the complexity of the drilling process, the use of artificial intelligence (AI) has been a game changer because most of the unknown parameters can now be accounted for entirely at the modeling process. The objective of this paper is to evaluate the ability of the optimized adaptive neuro-fuzzy inference system (ANFIS), functional neural networks (FN), random forests (RF), and support vector machine (SVM) models to predict the ROP in real time from the drilling parameters in the S-shape well profile, for the first time, based on the drilling parameters of weight on bit (WOB), drillstring rotation (DSR), torque (T), pumping rate (GPM), and standpipe pressure (SPP). Data from two wells were used for training and testing (Well A and Well B with 4012 and 1717 data points, respectively), and one well for validation (Well C) with 2500 data points. Well A and Well B data were combined in the training-testing phase and were randomly divided into a 70:30 ratio for training/testing. The results showed that the ANFIS, FN, and RF models could effectively predict the ROP from the drilling parameters in the S-shape well profile, while the accuracy of the SVM model was very low. The ANFIS, FN, and RF models predicted the ROP for the training data with average absolute percentage errors (AAPEs) of 9.50%, 13.44%, and 3.25%, respectively. For the testing data, the ANFIS, FN, and RF models predicted the ROP with AAPEs of 9.57%, 11.20%, and 8.37%, respectively. The ANFIS, FN, and RF models overperformed the available empirical correlations for ROP prediction. The ANFIS model estimated the ROP for the validation data with an AAPE of 9.06%, whereas the FN model predicted the ROP with an AAPE of 10.48%, and the RF model predicted the ROP with an AAPE of 10.43%. The SVM model predicted the ROP for the validation data with a very high AAPE of 30.05% and all empirical correlations predicted the ROP with AAPEs greater than 25%.

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