We’ve collected some of our best words of wisdom into these downloadable brochures, technical papers, and podcasts.

Product sheet

• SPOTTER Stuck Pipe Prediction agents,  product brochure (2022) Download
• SPOTTER ROP Real-time Optimization agent, product brochure (2022) Download
  
  
  

Industry articles and presentations

• Machine Learning Whitepaper Download
• Digitalization: a pragmatic approach, whitepaper (2020) Download
• Digital, detailed operating procedures, presentation (Darcy Partners “Drilling: Knowledge Management Forum”) YouTube
• Stuck pipe? Quash downtime with machine learning, presentation (IADC 2021) YouTube
• Digital operating procedures – prerequisite to automation, presentation (OTC 2021) YouTube
• Exebenus Pulse, product brochure (2021) Download
  
  

Case studies

SPOTTER significantly reduces drilling time on ultra-deepwater exploration well

Location Gabon, West Africa Ultra deepwater Challenge Show that real-time rate of penetration (ROP) optimization, can significantly contribute to improving ROP, decreasing drilling time and reducing costs, even when an auto driller is used. Solution: SPOTTER ROP agent was used by RTO engineers to provide the rig crew with real-time drilling parameter recommendations for optimizing…
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Real-time predictions and risk awareness help prevent nonproductive time

Location Magdalena River Valley Basin, South America Challenge Differential sticking is a known risk in the depleted reservoirs of the mature Magdalena River Valley basin, causing operators significant nonproductive time (NPT) during drilling and tripping operations. Solution: Monitor real-time feed remotely using SPOTTER software. Provide risk awareness of situations that can cause stuck pipe…
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ROP optimization agent reduces drilling time in offshore development side-track well

Location Offshore, deepwater, Malaysia Challenge Improve formation insight and increase drilling performance in a clay and sandstone environment known to cause slow rate of penetration (ROP). Solution: Field trial real-time SPOTTER ROP optimization agent in a side-track well section. Run stuck pipe hole cleaning agent to understand risk of increased cuttings in suspension and cave-ins. Results…
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National oil company uses Machine Learning to steer through hazardous, high-dogleg intervals while running 9 5/8” casing

Predictive machine learning agents were used by the real-time operation center to make informed decisions and guide the rig crew through hazardous, high dogleg and high inclination intervals.

National Oil Company (NOC) experienced a stuck pipe while drilling the main wellbore of a production well, and as a consequence needed to drill a costly side-track to reach the target. The side-track required casing to be run through two high dogleg-severity (DLS) and high inclination intervals, adding further risk of stuck pipe and cost increases.
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Technical papers

Leveraging Targeted Machine Learning for Early Warning and Prevention of Stuck Pipe, Tight Holes, Pack Offs, Hole Cleaning Issues and Other Potential Drilling Hazards

Tim S. Robinson, Vlad K. Payrazyan

2023 Offshore Technology Conference, Houston TX, USA

Paper Number: OTC-32169-MS

Abstract: Stuck pipe and other related drilling hazards are major causes of non-productive time while drilling. Being able to spot early indications of potential drilling risks manually by analyzing drilling parameters in real-time has been a significant challenge for engineers. However, this task can be successfully executed by modern data analytics tools based on machine learning (ML) technologies. The objective of the presented study is to prove and demonstrate the ability of such machine learning algorithms to process and analyze simultaneously a variety of surface drilling data in real-time in order to: a) detect anomalies, that are in most cases invisible to a human eye; and b) provide early warnings of possible upcoming drilling risks with sufficient time in advance, so that the rig crew can execute the appropriate mitigation actions.

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Application of Machine Learning to Augment Wellbore Geometry-Related Stuck Pipe Risk Identification in Real Time

Eswadi Bin Othman; Dalila Gomes; Tengku Ezharuddin Bin Tengku Bidin; Meor M. Hakeem Meor Hashim; M. Hazwan Yusoff; M. Faris Arriffin; Rohaizat Ghazali

Paper presented at the Offshore Technology Conference Asia, Virtual and Kuala Lumpur, Malaysia, March 2022. Paper Number: OTC-31695-MS

Abstract: Wellbore geometry stuck pipe mechanism occurs when the string and the well are incompatible with each other. This issue is commonly related to changes in hole diameter, angle, and direction associated with symptoms such as mobile/swelling formation, undergauged hole, key seating, ledges, and high doglegs. An internal study identified that many stuck pipe incidents were associated with mechanical sticking, specifically wellbore geometry sticking with high-cost impact, which warrants proactive prevention. Throughout this paper, we provide and demonstrate how machine learning solutions can foresee the potential stuck pipe related to wellbore geometry issues based on two signs: hookload signature and dogleg severity.

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

SPE-208675-MS: Tim S. Robinson, Exebenus. 

Abstract: 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.
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Unsupervised machine learning: A well planning tool for the future

OMAE2022-78423: Peter Batruny, PETRONAS Carigali Sdn Bhd, Tim Robinson, Exebenus

Abstract: 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.
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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

Abstract: 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.
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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

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.
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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

Abstract: 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.
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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

Abstract: 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.
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An Electronic Rig Action Plan – Information Carriers 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.

Abstract: 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.
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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.

Abstract: 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.
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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.

Abstract: 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.
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Podcasts