Digitalized Operation Procedures Provide Rig Automation System With Context To Manage Longer And Broader Sequences Of Activities

SPE-208767-MS: Pourya Farmanbar, Anne Siw Berge, Olav Revheim, Alexander Chekushev, Serafima Schaefer, Exebenus. 

Abstracts: The exact definition of all types of activities in well construction, from spud to completion, is an area of great challenges for an automation system to function successfully in. In an operation plan, these activities can be categorized into three subgroups: standard and repetitive sub-activities, customized sub-activities, and manual sub-activities. A digitalized detailed operation procedure (DOP) provides the appropriate context by defining the machine-readable version of these activities.

Real-time Estimation Of Downhole Equivalent Circulating Density (ECD) Using Machine Learning And Applications

SPE-208675-MS: Tim S. Robinson, Dalila Gomes, Exebenus;  Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Tengu Ezharudin, Eswadi Othman, Faazmiar Technology SDN BHD

Abstracts: Despite many drilling technology improvements during recent years, hole cleaning remains a significant challenge. The variation of equivalent circulation density (ECD) is a symptom of borehole instability. Therefore, the ability to accurately estimate ECD is a key consideration for preventing hole cleaning problems that may lead to a stuck pipe, and well pressure management more generally.

Unsupervised machine learning: A well planning tool for the future

OMEA2022-78423: Peter Batruny, PETRONAS Carigali Sdn Bhd, Tim Robinson, Exebenus

Abstracts: In recent years, the industry has sought insights from abundant data generated by drilling operations. One of the key focus areas is the rate of penetration (ROP) which impacts costs directly, and emissions indirectly. Previous work has succeeded in predicting and optimizing ROP, however was limited to specific fields and small-scale applications. This limitation stems from unobserved information between different fields or operations that often impacts model usability. This paper provides a new way of well planning by leveraging the power of unsupervised machine learning to deliver higher drilling efficiency, lower costs, and less uncertainty.

Successful Development and Deployment of a Global ROP Optimization Machine Learning Model

OTC-31680-MS: Timothy S. Robinson, Dalila Gomes, Exebenus, Peter Batruny, Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd

Abstracts: Drilling rate of penetration (ROP) is a major contributor to drilling costs. ROP is influenced by many different controllable and uncontrollable factors that are difficult to distinguish with the naked eye. Thus, machine learning (ML) models such as neural networks (NN) have gained momentum in the drilling industry. Existing models were either field-based or tool-based, which impacted the accuracy outside of the trained field. This work aims to develop one generally applicable global ROP model, reducing the effort needed to re-develop models for every application.

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

IPTC-21199-MS: Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Tengku Ezharuddin Tengku Bidin, Faazmiar Technology Sdn Bhd; Dalila Gomes, Exebenus

Abstracts: 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.

Performance Improvement of Wells Augmented Stuck Pipe Indicator via Model Evaluations

IPTC:21455-MS: Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Tengku Ezharuddin Tengku Bidin, Faazmiar Technology Sdn Bhd; Dalila Gomes, Exebenus

Abstracts: The advancement of technology in this era has long profited the oil and gas industry by means of shrinking non-productive time (NPT) events and reducing drilling operational costs via real-time monitoring and intervention. Nevertheless, stuck pipe incidents have been a big concern and pain point for any drilling operations. Real-time monitoring with the aid of dynamic roadmaps of drilling parameters is useful in recognizing potential downhole issues but the initial stuck pipe symptoms are often minuscule in a short time frame hence it is a challenge to identify it in time. Wells Augmented Stuck Pipe Indicator (WASP) is a data-driven method leveraging historical drilling data and auxiliary engineering information to provide an impartial trend detection of impending stuck pipe incidents. WASP is a solution set to tackle the challenge. The solution is anchored on Machine Learning (ML) models which assess real-time drilling data and compute the risk of potential stuck pipe based on drilling activities, probable stuck pipe mechanisms, and operation time.

Leveraging Machine Learning Model For Real-Time Prediction of Differential Sticking Symptoms

ITPC-21221-MS: Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Dalila Gomes and Majo Jose, EXEBENUS; Tengku Ezharuddin Tengku Bidin, FAAZMIAR TechnologySdn Bhd

Abstracts: Stuck pipe is one of the leading causes of non-productive time (NPT) while drilling. Machine learning (ML) techniques can be used to predict and avoid stuck pipe issues. In this paper, a model based on ML to predict and prevent stuck pipe related to differential sticking (DS) is presented. The stuck pipe indicator is established by detecting and predicting abnormalities in the drag signatures during tripping and drilling activities. The solution focuses on detecting differential sticking risk via assessing hookload signatures, based on previous experience from historical wells. Therefore, selecting the proper training set has proven to be a crucial stage of model development, especially considering the challenges in data quality. The model is trained with historical wells with and without differential sticking issues.

An Electronic Rig Action Plan – Information Carrier Equally Applicable to the Driller and the Automation Platform

SPE-195959-MS, Lars-Jørgen Ruså Solvi, Aker BP, Olav Revheim, Exebenus, Serafima Schaefer, Exebenus, Frank Johan Schutte, Aker BP.
Presented at SPE ATCE 2019.

Abstracts: By use of the proposed method for digitilizing operation procedures and activities, the rig action plan can become the dynamic information exchange platform between planning and execution phase. Digitilizing the workflow and structuring the information in a rig action plan enables engineers to plan operations and transmit procedures and related parameters in a consistent form applicable to the driller and the drilling control system’s automation platform.

Digitalization of Detailed Drilling Operation Plans and Verification of Automatic Progress Tracking with an Online Drilling Simulator Environment

SPE-199666-MS, Pourya Farmanbar, Exebenus, Olav Revheim, Exebenus, Anne Siw Uberg, Exebenus, Alexander Chekushev, Exebenus, Eric Cayeux, NORCE, Jan Einar Gravdal, NORCE, Espen Hauge, Equinor. Presented at SPE/IADC 2020.

Abstracts: In the age of digitalization and automatization of drilling operations, it is time to move the detailed drilling operating procedure from its classical text format, only intended for human interpretation, to a structured representation that can be utilized efficiently by computer systems. The immediate benefit of this transformation is that progress tracking during an operation can be fully automated.