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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204043-MS
... differential pressure is directly related to the real-time PDC WOB, which passes the sniff test that actual weight at the bit should be a major factor in determining ROP. Abstract This paper is a follow up to the URTeC (2019-343) publication where the training of a Machine Learning (ML) model to predict...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204051-MS
... planning, helps the drilling team to get updated drilling information and perform risk management prior to drilling. machine learning log analysis geologic modeling drilling measurement well logging reservoir characterization drillstring design geological modeling drilling data acquisition...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204093-MS
.... reservoir characterization logging while drilling drilling data acquisition drilling measurement deep learning accuracy neural network muddy micaceous sandstone upstream oil & gas drilling parameter virtual log model lwd structural geology micaceous sandstone stringer machine learning...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204098-MS
... acquisition and processing are performed at 31,250 Hz, providing enough bandwidth to fully reconstruct high-frequency events. A novel methodology combining field incidents with machine learning clustering algorithms is proposed to identify arrhythmic shocks signatures and whirl and bit bounce in real time...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204094-MS
... is initiated, including after a connection or after a prolonged break in drilling operations. It is important to be able to predict the magnitude of such pressure spikes to avoid compromising wellbore integrity. This study shows how a hybrid approach using data-driven machine learning coupled with physics...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204099-MS
... ≤ α), the null hypothesis is rejected, and the feature is said to be statistically significant. Else, if p > α, the hypothesis is not rejected because there is not enough evidence to reject the null hypothesis and the result is deemed inconclusive. A supervised machine-learning (ML...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204105-MS
... on machine learning, i.e., the reinforcement learning method. In the decision-making process, the agent gained the ability to steer through random exploration in the simulation environment. The optimization of the decision is not on a single-step reward, but the total future rewards. By estimating the total...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204122-MS
... is illustrated for two synthetic case studies. artificial intelligence well logging log analysis directional drilling geological interpretation automated computational framework reservoir navigation upstream oil & gas information optimization problem geosteering objective function machine...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204108-MS
... on the machine learning approaches like the random forest, gradient boosting, support vector machines, logistic regression, polynomial regression, and artificial neural network to evaluate the applicability of each of these approach in optimizing the power consumption using the control variables like RPM...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204086-MS
.... This technology involves vision analytics. Currently, detection algorithms rely heavily on data collected by sensors installed on the rig. However, relying exclusively on sensor data is problematic because sensors are prone to failure and are expensive to maintain and install. By proposing a machine learning...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204124-MS
... damage bit image captured algorithm drilling conference petroleum engineer dataset machine learning neural network bit image classifier whirl expert opinion Drill bit damage detection and failure analysis (i.e. forensics) are an important part of drilling process. By identifying...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204078-MS
... expect will bring improved clarity on the annulus behavior. We explain the rationale for such an approach by providing a catalog of log response for the seven classes. In addition, we introduce the ability to carry out such analysis autonomously though machine learning. Such machine learning algorithms...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204101-MS
.... The expected total volume of cavings is determined using a machine learning (ML) assisted 3D elasto-plastic finite element model (FEM). The FEM works to model the interval of interest, which eventually provides a description of the stress distribution around the wellbore. The ML algorithm works to learn...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204097-MS
... oilfield chemistry machine learning production chemistry directional drilling high pressure high temperature completion heat transfer fracture complex reservoir renewable energy engineering application petroleum engineer reservoir characterization hpht drilling operation ashok...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204125-MS
... by a machine learning model in the form of a Bayesian network that predicts hole conditions based on evidence from surface data. The network conveys the conditional independence and conditional probabilities of several different features related to hole cleaning. Figure 1 is a representation of the Bayesian...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204035-MS
... 2012 ), statistical approaches, or, more recently, supervised machine learning techniques ( Kuesters 2020 ). Advanced flow modeling approaches might suffer from context unavailability, poor accuracy, and high sensitivity to a lot of drilling parameters (mud properties, drilling tool description...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204053-MS
... taking the prior knowledge into consideration. machine learning bayesian inference drilling measurement optimization problem drilling data acquisition drilling operation stratigraphy typelog objective function artificial intelligence upstream oil & gas gr typelog waypoint improved...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204064-MS
... describes the hybrid test environment and key learnings from the developers and user's perspective. well control drilling fluids and materials demonstration simulation simulator machine learning artificial intelligence upstream oil & gas hil simulator exhibition norway application real...
Proceedings Papers

Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 8–12, 2021
Paper Number: SPE-204063-MS
... they reach surface sensors. Building machine learning models to recognize patterns in the surface data requires vibration signals captured by downhole sensors for training purposes. Such datasets are not widely available and therefore a methodology to expand these datasets is highly desirable. This work...
Proceedings Papers

Paper presented at the IADC/SPE International Drilling Conference and Exhibition, March 3–5, 2020
Paper Number: SPE-199584-MS
..., more reliable data-driven decisions to optimize performance and directional control of the well path. Artificial Intelligence Directional Drilling machine learning Drilling Equipment Upstream Oil & Gas time series data neural network deep learning real-time deep learning model drive...

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