Summary This paper describes the results of a field test, conducted at a test rig, of a hole cleaning and wellbore stability monitoring system. This system encompasses a laser-based sensor and digital tools designed to process the collected data in real time. Additionally, this paper outlines the method for interpreting the processed data, including the combination of the estimated volume of cuttings recovered at the surface and the expected volume obtained with a transient cuttings transport model. The test was conducted while drilling three borehole sections at a test location in Germany. The preparation for the test included the installation of an auger system to divert the cuttings from three active shale shakers to the cuttings sensor. This sensor is equipped with a high-definition camera and a laser profile scanner, enabling the collection of both 2D images and point-cloud data (PCD) of the cuttings stream. These data were then fed into a digital tool capable of estimating the solids’ volume and morphological characteristics, identifying cavings and their shape and size, and providing an evaluation of the wellbore condition. The test revealed that the system can be successfully deployed in the field with a simple rig-up procedure. Additionally, it showed that the digital tool and the underlying machine-learning techniques can accurately estimate the solids’ volume and their morphological characteristics. Furthermore, a minor discrepancy between the measured volume and the expected volume according to a transient cuttings transport model showed that the sensor can reliably be used to assess hole cleaning sufficiency in real time. During these tests, artificial cavings were introduced to evaluate the system’s capability of cavings detection and characterization, the latter involving the shape of cavings, which is related to the wellbore failure mechanism that created them. The results indicate that the system can be used to evaluate the in-depth nature of wellbore stability in real time. This, in turn, will allow for targeted corrective action to deal with stability-related operational problems. Finally, we also identified limitations of the current system, such as the generation of nonoptimal segmentation results when the cuttings’ size ranges from medium to coarse sand size (0.25–1 mm). Based on these limitations, we have defined a plan for future improvement, further described in detail in this paper. Previous field testing of the sensor only evaluated the sensor’s readiness for deployment and the software’s capability to reliably measure bulk volume—largely without correction for void space. The tests presented in this paper are more comprehensive. They evaluated the system’s capabilities to measure the corrected solids’ volume, their morphological characteristics, and the integration of a transient cuttings model into the system for a holistic assessment of the well condition.

Abstract Recognition of which drilling activity is occurring at the wellsite is important for calculating key performance indicators (KPIs), diagnosing potential issues during a drilling operation, and providing advice to the automated drilling control system (ADCS). The drilling team is aware of the current drilling activity at any given time, and daily drilling reporting systems categorize drilling operations into activities and sub-activities. Recognizing which drilling activity is being executed in real-time by a computer system is, however, a challenging task. A method has been proposed to uniquely characterize the drilling process state at any instant. This method relies on the decomposition of the possible boundary values of the drilling process into so-called micro-states. The combination of the values of all the micro-states constitutes the drilling process state. The process of estimating the value of each micro-state can be computerized using inputs from measurements and estimations from digital twins. The succession of certain patterns in the flow of drilling process states is characteristic of a specific drilling activity. By recognizing these patterns, it is possible to categorize the current drilling activity. The drilling process is a hierarchical construct, and layers of activities with drilling equipment yield layers of drilling operations. Since the construct is hierarchical it is possible to formulate rules about the relationships between drilling process states and how they characterize drilling activities. In the digital space, a similar technique is Backus-Naur Form (BNF), a standard technique used to describe valid syntax of computer programs in a programming language. BNF rules can define what constitutes a low-level drilling activity, and also specify that only certain combinations of low-level drilling activities make sense. This allows defining drilling activities and sub-activities in a computer-interpretable manner. The use of BNF rules simplifies the implementation of a parser that categorizes drilling activities in real-time using a classical pushdown automaton. Any future adjustments to these rules to accommodate new drilling methods and techniques do not require recoding the drilling activity parser. If there are insufficient states to uniquely identify the drilling activity then the parser can also generate the set of possible drilling activities matching the set of identified states, and it can be used to pro-actively trigger a desired activity based on an entry, for example, in the rig action plan. This work is part of the Drilling and Wells Interoperability Standards (D-WIS) sub-committee of the Drilling System Automation Technical Section (DSATS). The D-WIS infrastructure provides shared functionalities to facilitate the collaboration of multiple applications during drilling operations. Adding a shared and clearly defined classification of drilling activities allows for high-level synchronization of independent multiple agents working together to enhance drilling process efficiency.

The progress of drilling systems automation has accelerated over the last decade. A myriad of applications dealing with process optimization, wellbore protection, and automated directional drilling have been deployed in a more frequent and stable manner and have become mainstream in some regions. The deployment of drilling automation applications alongside traditional operations has created significant value in terms of avoiding non-productive time (NPT), reducing invisible lost time (ILT) and optimization of drilling performance to deliver more accurate well placement while maintaining wellbore and equipment integrity. While drilling automation on its own involves a large deal of modeling and data processing, it rests on the shoulders of well planning and drilling engineering. Traditionally, well planning and drilling engineering activities take place in a pre-well context to define a reference for the upcoming well. However, since drilling automation applications operate in real-time, the context provided by well planning and drilling engineering activities has been taken into the real-time realm. As a result, well planning constantly evolves while drilling to recalculate wellbore trajectories that are compliant with magnetic interference, anti-collision and overall tortuosity. These updated wellbore trajectories are the basis for automated directional drilling. Likewise, drilling engineering simulations involving physics-based digital twins of the wellbore are updated with live feed of sensor data to produce context for advisory and closed-loop control services for wellbore protection. For instance, real-time time drilling engineering produces safe operating envelopes ( SOE) for surface parameters, estimates cuttings transport in the wellbore, and calculates reference parameters to automate certain processes at the rig site, such as the pump-startup. The role of drilling engineering and well planning in enabling drilling automation applications is of utmost importance. This paper discusses three main areas of well engineering (well planning plus drilling engineering) and the associated drilling automation applications that benefit from it. The first area is well planning as the basis for automated directional drilling. The second touches upon hydraulics modeling as the reference for real-time advisory services dealing with pressure management, hole cleaning, and tripping monitoring. The last area of focus is torque & drag modeling as a roadmap to monitor hazards downhole and to provide an SOE to ensure the mechanical integrity of the equipment.

Abstract Drilling systems automation (DSA) involves multiple actors, each delivering functionality at different levels of automation, with system performance dependent on various input from human operators. Current automation classifications do not fully address the multi-agent nature of drilling operations. Marketing language in industry publications has also outstripped reality by boldly describing automated drilling operations as autonomous, leading to confusion. There is a need to define and include autonomous behavior in the taxonomy of drilling systems automation. A completely autonomous system without direct human interaction may not be a practical goal. Classification into levels of automation for drilling applies to the union of all functions used in a particular operation, and their interaction with humans. Various developed taxonomies showing the transition from manual to highly automated systems use the construct: acquire/observe, assess/orient, decide and act. This paper presents and analyzes taxonomies for their applicability to drilling systems automation, and their use to describe the level of autonomy in this discipline, considering the multi-agent nature and weak observability of drilling operations requiring human consideration. The authors initially collaborated under the SPE DSATS (Drilling Systems Automation Technical Section) to develop a classification applicable to drilling systems automation — and by extension, completions, intervention, and P&A automation — in which autonomous systems are recognized. The classification distinguishes the multi-agent drilling environment in which one agent may be concerned with hole cleaning, another with automated trajectory drilling, and yet another with optimizing rate-of-penetration, all while acting interdependently. Depending on the necessary collaboration between agents, this multi-agent construct can lead to a mixed-initiative autonomous system that is able to handle the complexity and uncertainty of the drilling environment. Drilling, however, also has an observability problem that necessitates a more stratified solution to taxonomy due to missing or lacking data and data attributes. This observability problem exists in both space and time: most measurements are at surface, some from the bottomhole assembly; the low bandwidth of traditional measurement-while-drilling telemetry methods delivers sparse measurements. This paper recommends a taxonomy for drilling systems automation from an enterprise to an execution level that considers the observability problem, complexity, and uncertainty, delivering the necessary capability to accurately classify and address autonomy within drilling systems automation. This taxonomy will greatly reduce the chance of miscommunication regarding drilling system automation capabilities. The complexity, uncertainty, and sparse observability inherent in drilling suggests that the levels of automation taxonomies adopted in other industries (aviation, automotive, etc.) may not appear directly applicable to drilling systems automation. However, the introduction of three levels of autonomous systems leaves the application of a drilling systems automation levels of taxonomy as an underlying model. A clearly communicated safe introduction of automated and autonomous drilling systems will directly benefit from this industry-specific taxonomy that recognizes the degree of needed human interaction at all levels across all interconnected systems.

Automation and digitalization of drilling requires shared knowledge about the state of the drilling process: is the bit on-bottom drilling or is the driller making a connection; is the borehole in good condition or is it sloughing? Yet there is no shared, clear and usable definition of what a drilling process state is, nor an agreed method to calculate it. In this paper, we propose a method to clarify the concept of drilling process state. A set of partial differential equations, respecting boundary conditions, can describe drilling operations. The set of all possible discrete changes of boundary conditions, therefore, defines the set of all possible drilling process states. Equality or inequality of logical expressions of at most two boundary values characterizes a discrete change of a boundary condition. For instance, if forces applied to the bit by the formation are zero, this corresponds to an off-bottom condition, while forces greater than zero means that the bit is on-bottom. Such simple logical conditions are microstates, and an orthogonal set of microstates defines a drilling process state. An analysis of the drilling process from the perspective of these microstates defines an orthogonal basis of microstates. It is possible to define uniquely any drilling process state in this orthogonal basis. There are a finite number of possibilities to move from one state to a different state by changing only one single microstate, which leads to the construction of an implicit graph of possible states. In this implicit state graph, the change from one state to another state that corresponds to more than one modification of the microstates corresponds to a path in the graph. However, the microstate basis depends on the type of drilling process. The paper will provide examples of different microstate bases for conventional drilling, backpressure managed pressure drilling, and dual-gradient managed pressure drilling. Microstates also cover abnormal drilling conditions, such as hanging on a ledge, or flow obstruction in the annulus by a pack-off. They are, therefore, more powerful descriptors than "rig activity codes". The required fidelity of the drilling process state depends on its use, for example for controlling drilling equipment (process control), for calculating key performance indicators (process statistics), or for user feedback (human factors engineering). This work is part of the D-WIS initiative (Drilling and Wells Interoperability Standard). D-WIS is a cross-industry workgroup providing the industry with solutions facilitating interoperability of computer systems at the rig site. The definition of a microstate is a simple logical statement, easily implemented in computer software. The paper provides an example of a simple algorithm, which will enable others to leverage the work in the commercial, interoperable, environment.

Abstract Drilling automation and remote operations are driving advisory systems to provide a safe operating environment, protect the wellbore, and optimize efficiency at the wellsite. These interoperable systems deliver guidance to control systems, for example set points and limits for drilling parameters. The Drilling and Wells Interoperability Standard Industry Group (D-WIS) under the SPE Drilling Systems Automation Technical Section (DSATS) wants to accelerate interoperable system implementation by outlining a compelling value case. The adoption of drilling operation automation systems is increasing operational efficiency and opening new opportunities to manage drilling with advisory systems. These systems are the focus of efforts in interoperability (i.e., plug-and-play functionality) to remove data communication obstacles and allow the implementation of the next generation of advisory and process control technology. This paper describes the technology focus, status, and impact of the current industry efforts and organizations contributing to interoperable systems regarding the core system components and standardization needs. The value case is made for an interoperability standard suitable for large scale industry deployment. The current industry state reveals custom-made supplier solutions continuing to expand and evolve with limited applicability due to a bespoke approach. The authors observe and contrast this method with an interoperable system approach to make the value case in terms of easier and faster implementation and scalability, better reliability and access to data, and lower development and operations cost, resulting in a higher overall value. A comparison of the two competing scenarios along with past industry efforts toward standardization clarifies how to best realize the vision of safely and efficiently sharing information and facilitating drilling operations. Collaborative efforts between industry organizations also help address areas of overlap and gaps related to interoperability and creation of a standard. The conclusions outline the minimum requirements of a focused industry effort to deliver an implementation-ready system. System adoption is accelerated by creating deliverables fit for roadmaps and specific collaboration points. The paper examines the effectiveness of current interoperable system efforts. It describes and demonstrates industry influencing work towards interoperable drilling systems and builds on a collection of industry work beyond Sadlier and Laing 2011, and Macpherson et al. 2013.

Abstract Well construction requires the cooperation of subject matter experts with differing backgrounds. Each discipline has its own viewpoint on the topics addressed by the collaboration team, which complicates multi-disciplinary work. Software tools often embed these differences, rendering the exchange of data subject to the assumptions of the differing backgrounds and disciplines. This can pose significant risk to well, human, asset value and environmental safety. Well construction software applications need to be versatile enough to describe and interpret the meaning of exchanged data while allowing for the inherent assumptions. Facts about data best describe the meaning of the exchanged data. A semantic network can describe the relationships between concepts or meanings, which it does by collecting and relating facts about the data. The existing data repositories used in well construction use predefined and agreed upon meanings for the contained data. A semantic network, however, allows software applications to interpret the meaning of the transferred data, even when the original meaning and inherent assumptions are different from that of the receiving application. Obviously, the manual generation of such semantic information by end-users is not practical. Each software application needs to generate and interpret semantic information alongside the published data. A simple example illustrates the potentially costly consequences of the differing meanings of exchanged information in a multi-disciplinary well construction environment. Three different end-users—a geomechanics engineer, a drilling engineer, and a measurement-while-drilling (MWD) engineer—exchange information, using computer applications, about downhole pressure measurements acquired during a drilling operation. The downhole pressures are in equivalent mud weight (EMW) units. Due to the different disciplines involved, each application utilizes a different definition when converting downhole pressure to EMW. The assumptions related to the accuracy of not only the mud density but also the vertical depth, then create expectations that may not be in line with reality, and each of the three different users themselves have different uncertainty expectations. These different definitions require information on context to avoid misinterpretation. If the three applications can document the meaning of the exchanged data and can interpret the meaning of the received semantical information, then they can mitigate the risk of incorrect interpretation of the EMW data and provide quantification of the associated uncertainties. The scenario described also covers a change in context from conventional drilling to a well control incident. A different interpretation of the meaning of the data must follow the change in context. The paper explains how the three applications can describe the meaning of the exchanged data in order to correctly interpret information and manage the associated uncertainties both in terms of expectations as well as results. Digitalization of the drilling industry is progressing rapidly, accompanied by the ability to exchange information between computer systems. This clearly breaks down the walls between the different disciplines involved in well planning and drilling operations. It is also important to recognize the uncertainty expectations between disciplines. These expectations must be managed so that the assumptions made are harmonious. To maximize potential opportunities, it is important to maintain the quality of information exchanged between computer systems to mitigate the introduction of significant risk to well, human and environmental safety.

Geopressure estimation is an important aspect of well planning and execution. However, there are many sources of uncertainty that can affect the accuracy and timing of the prognosis. These uncertainties are associated with data produced by many different disciplines at various times throughout the life of the well. As subject matter experts tend to work in silos these uncertainties are often unshared, and there is no appropriate routine performance of uncertainty propagation across disciplines. This can negatively affect decision making during both the engineering and operational phases of a well. Uncertainty requirements across disciplines are often not formulated into coherent uncertainty management. It is therefore important to understand the possible sources of uncertainty to better quantify the estimation of geopressures and to make smarter decisions. This paper describes the uncertainties associated with each estimate of geopressure, their locations in the multi-discipline silos, and the current relationship between estimates. With this comes the realization of a structure or method for combining the individual uncertainties to provide a clearer idea of geopressure estimation and its inherent uncertainty. For instance, combining wellbore position uncertainty with the stratigraphic earth model uncertainty makes possible the estimation of the spatial probability distribution of particular geopressure related observations. The sources of information for geopressure prognosis are many, spread across disparate systems with various discipline ownership. Even direct and real-time observations of formation fluid influx, borehole collapse or formation fracturing can depend on the precision of downhole pressure measurements and knowledge. Extrapolate measured downhole pressures to positions far removed from the measurement point is often necessary. This requires accurate calculation of hydrostatic and hydrodynamic pressures and the wellbore and vertical depth positions to infer pressure profiles along the borehole. These profiles are a function of the accuracy of characterization of the pressure and temperature behavior of the drilling fluid properties and the well depth. Temperature estimations depend on definition of geothermal gradients and the precision of heat transfer calculations causing a varying degree of accuracy for baseline profiles to base operational decision. It is possible to measure pore pressures in situ, or to estimate them using trend analysis of formation evaluation or drilling logs. Factors influencing the precision of the results include the actual measurement depth value uncertainty, and the impact of wellbore position uncertainty on their correlation with an earth model. Leak-off tests deliver information about geopressure margins, but the interpretation of flow-back measurements creates further uncertainties that propagate across the prognosis. The propagated uncertainties from all these sources can be derived using stochastic simulations, yielding, when combined, a quantitative assessment of geopressures. In addition, Kriging methods can incorporate new geopressure estimations in a geomechanics oriented earth model. The paper provides a list of possible sources of uncertainties and a possible categorization of their origins. It describes the causal links between the sources of uncertainties and their effect on the quality of geopressure prognosis. The purpose is to facilitate the adoption of quantitative uncertainty assessment methods by the well construction community for geopressure estimations.

Drilling oil and gas wells is a complex process involving many disciplines and stakeholders. This process occurs in a context where some pieces of information are unknown, or are often incomplete, erroneous, or at least uncertain. Yet, during drilling engineering and construction of a well, drilling data quality and uncertainty are barely addressed in an auditable and scientific way. Currently, there are few or no placeholders in engineering and operational databases to document uncertainty and its propagation. The Society of Petroleum Engineers (SPE) has formed a cross-disciplinary technical subcommittee to investigate how to describe and propagate drilling data quality and uncertainty. The subcommittee is a cooperation between the drilling system automation, wellbore positioning, and drilling uncertain-ty prediction technical sections. As the topic is vast and complex, a systematic method was adopted, where multiple user stories or pain points were generated and ranked with the most compelling user story analyzed in detail. From this approach, a series of multidisciplinary workflows (drilling data generators) can now be captured and described in terms of data quality and propagation of uncertainty. The paper presents details of one user story focused on capturing the description of the quality and uncertainty of depth measurements. Multiple use cases have been extracted from this single user story exemplifying how multiple stakeholders and disciplines manage, communicate, and understand the notion of wellbore depth and its relative uncertainty. Current data stores have the main objective of recording the results of processes but have very limited capabilities to store how the interdisciplinary processes generated and cross-related these results. The study explores the use of semantic networks to capture those multidisciplinary data relationships. A minimum vocabulary has been created using just a few tens of concepts that has sufficient expressiveness to describe all the extracted use cases, showing that the semantic network method has the potential to describe a broad range of complex drilling-related processes. The study also demonstrates that use of a multilayered graph, employing other notions that do not expressly refer to the processes that generated the data, can capture the description of how uncertainty propagates between each of those concepts.

Due to the nature of drilling operations, there are several companies collecting data at the rig. The data acquisition system of each com-pany applies its own timestamp to the data. Subsequent aggregation of data (for example, in a data repository) relies on synchronized timestamps applied to the different data sources to correctly collate the data. Unfortunately, synchronized timestamping is rarely achieved. In this paper, we document the different sources of errors in timestamping of data and provide guidelines to help mitigate some of these causes. There are many reasons for the unsynchronized timestamping of data from different sources. It can be as simple as clock synchronization at the rig; each data-providing or-producing company has an independent clock. It can also be due to where the timestamp is applied, for example, at the data source or on data reception. Additionally, it can be due to how the timestamp is applied-at the start of the sampling interval, the midpoint, or the end. Some of the communication methods used at the wellsite, such as mud pulse telemetry that is used to transmit downhole measurements to the surface, have a high, nonstationary latency and the actual acquisition time may vary significantly from the received time. Not correcting the reception time for the transmission delay can result in erroneous timestamping of downhole-acquired data.Timestamping of derived data (data computed from two or more sources) is problematic if the data sources are unsynchronized.Synchronization of clocks within the data acquisition network is therefore extremely important. The resolution of time synchronization depends on purpose; motion control of the rig equipment (for example, the hoist) demands high-resolution timekeeping. However, for the purposes of timestamping acquired data, synchronization to a network time server (a computer with access to a reference clock that distributes the time of day to its client computers over a network) with a resolution of 1 millisecond is sufficient. The issue is agreeing on the common source of time (the reference clock) and agreeing on the passage of time signals through network firewalls. Timestamping is a more involved matter, calling for agreement on standards and, if possible, a computer-interpretable description of the time-related information associated with real -time data. In this paper, we describe in some detail sender vs. receiver timestamping, the downhole to surface timestamp chain, and timestamping of derived data. Systems automation and interoperability at the rigsite-allowing plug-and -play access to equipment and applications-rely on an agreed -upon network synchronization scheme and timestamping methods and standards. Indeed, designing applications that must handle uncertain time adds considerable complexity and cost, not to mention the impact on accuracy and reliability. We present an ordered approach (or guidelines) to a quite resolvable problem.In the last section of the paper, we use a semantic network approach (a semantic graph) to describe relationships for clock synchronization and timestamping (the guidelines and recommendations developed in this paper). A complete description of the semantic vocabulary is provided in an appendix. This makes these guidelines and recommendations digital-able to be interpreted by digital devices-and therefore implementable and auditable.

As we begin a new decade, drilling systems automation has left its primary residence of PowerPoint slides and is now seeing wider adoption. In areas, the industry sees positive return-on-investment for automation technology development; both financially, from reduction in well construction cost, and in helping meet increasingly prominent ESG targets through emissions reduction. This paper describes a long-term case study covering the introduction of multiple automated monitoring, advisory and control systems into an already highly optimized jack-up in the North Sea. These resulted in adding incremental value, delivering a multitude of operations significantly below AFE, as well creating a trend of increasing performance spanning a full field development campaign. In addition to quantifying resulting operational cost-out, the paper addresses several points related to challenges of adoption: –Necessity for un-biased operational needs to drive technology development. The industry is often drawn to high-end solutions to complex problems when in fact there are low-hanging fruits, such as simple business-process-automation for reporting, which are highly desired by end-users.–The notion that while there is a lot of rigor in technology development processes, there is not enough focus on the critical human element of adoption. Linked to this is the common misconception that automated systems require less training, when in-fact the opposite is true.–End-user resistance on initial introduction of a black-box system for automated directional drilling. Retroactive software development moves to more grey- or white-box systems, with an associated positive response in user acceptance.–Critically of interoperability between operator, OFS and OEM systems. How this will become more important as both closed-loop control systems, and linkages to enterprise level systems, proliferate. This long-term case study definitively demonstrates that automated systems add value. However, due to the human-component, management-of-change must be carefully considered as we scale adoption.

Digitalization of the drilling process has the potential to improve drilling data quality and consistency, providing support for drilling optimization, safety and efficiency. A significant barrier to realizing this potential is the data streams from the multitude of service companies, which changes almost daily, with variable definition of each of the real-time signals. This paper provides a solution to this problem: a method describing the semantics of real-time drilling signals in a computer readable format. For illustration, consider the calculation of mechanical specific energy (MSE) in drilling. It is possible to calculate a simple MSE signal in many ways, by using surface or downhole measurements, by applying corrections to the raw data, or by interpreting the equation in alternate ways. There is typically only a delivered value – the underlying details are lost. Semantic graphs bring transparency to the calculation by describing facts about drilling signals that are interpretable by computer systems. This semantic information encompasses details about signal measurement, and about signal calculation, correction, or conversion, yet all without exposing proprietary mathematical methods of calculation. It is possible, using semantic graphs, to assess the meaning and potential application of a signal, and whether or not the quality of the signal is suitable for its intended purpose. A semantic network relies on a vocabulary that defines a specific language dedicated to a particular topic, here drilling signals. The semantic network language is versatile: an existing language can describe new information and newly created signals. This provides a method meeting future needs without having to modify a standard constantly. In practice, each data provider exposes the meaning of its signals in the form of individual semantic networks. Merging these distinct semantic graphs provides a larger set of facts. This opens the possibility for synergies between independent data providers. For instance, applying logical rules infers new information. Since it is possible to query the semantic graph for signals that have certain properties, discovery of the most relevant signals at any time is feasible. By keeping track of modifications made to the semantic network during the drilling operation, it is also possible to post-analyze facts known about the available drilling signals, in an historic perspective. This is essential information for interpreting real-time data during offline data mining. This work is part of the D-WIS initiative (Drilling and Wells Interoperability Standards), a cross-industry workgroup providing solutions to facilitate interoperability of computer systems at the rig site and beyond. The D-WIS workgroup continues to develop the semantic vocabulary. The benefit of a computer interpretable description of the meaning of real-time signal is not limited to signals in real-time. Indeed, the method allows automatic data mining of historical data sets, facilitating the application of machine learning methods.

Drilling process automation solutions provide positive assistance to the driller, and increase consistency in execution of drilling procedures. However, in drilling automation the use of automated drilling advisors can reduce human operator situational awareness. Therefore, systems that automatically detect and react to drilling incidents must support the driller. These critical systems cover Fault Detection, Isolation and Recovery (FDIR) functions. This paper presents a method that facilitates the interoperability of drilling automation advisors for FDIR functions. Some drilling events happen so fast that mitigation (FDIR) must be implemented directly at the automated drilling control system (ADCS) level. Yet, FDIR functions often need dynamic parametrization from external sources since the ADCS may lack access to mandatory information needed for correct detection and mitigation of the incident. This requires interoperability, communications without human intervention, between the ADCS and the external sources of the parameters for the FDIR function. To interconnect the two sides of the problem, the ADCS describes its capabilities for fault detection and isolation and the external application, the automation Advisor, adapts to the exposed capabilities. On the one hand, the ADCS may implement various types of FDIR functions. On the other hand, external dynamic parameter functions may only address certain types of drilling incidents. Different ADCS providers implement such FDIR functionalities in different ways. Since this undermines the portability (interoperability) of the solutions provided by third party advisor applications, any drilling systems automation solution must address this communication issue. The simplest form of communication describes predefined capabilities, providing the ability to communicate based on an agreement about a set of statically defined possibilities. At an intermediate level of complexity, the ADCS describes its capabilities in a descriptive format that the external application interprets, and to which it can adapt. In the most advanced version, the ADCS describes that it allows the external parameter provider to configure the ADCS behavior to its needs. The paper describes a generic data model covering all three levels of the interface. Another implementation of the model is in the form of a micro-service that implements a REST API and exchanges Json formatted data objects. The latter is therefore agnostic to programming languages and computer platforms. This work is part of the D-WIS (Drilling and Wells Interoperability Standard) initiative advancing industry wells digital systems interoperability. D-WIS is a cross-industry workgroup providing the industry with solutions to facilitate interoperability of digital and computer systems at the rig site. The proposed solution delivers retrofitting ease for existing solutions but is sufficiently flexible to accommodate to new and not yet known FDIR functions. It is a key function for systems interoperability at the rig site, directly addressing situational awareness for the driller.

Abstract Drilling oil and gas wells is a complex process involving many disciplines and stakeholders. This process occurs in a context where some pieces of information are unknown, or are often incomplete, erroneous or at least uncertain. Yet, during drilling engineering and construction of a well, drilling data quality and uncertainty are barely addressed in an auditable and scientific way. Currently, there are few or no placeholders in engineering and operational databases to document uncertainty and its propagation. The SPE has formed a cross-disciplinary technical sub-committee to investigate how to describe and propagate drilling data quality and uncertainty. The sub-committee is a cooperation between the Drilling System Automation, Wellbore Positioning, and Drilling Uncertainty Prediction Technical Sections. As the topic is vast and complex, a systematic method was adopted, where multiple user stories or pain points were generated, and ranked with the most compelling user story analyzed in detail. From this approach, a series of multi-disciplinary workflow - drilling data generators - can now be captured and described in terms of data quality and propagation of uncertainty. The paper presents details of one "user story" focused on capturing the description of the quality and uncertainty of depths. Multiple "use cases" have been extracted from this single "user story" exemplifying how multiple stakeholders and disciplines manage, communicate, and understand the notion of wellbore depth and its relative uncertainty. Current data stores have the main objective of recording the results of processes but have very limited capabilities to store how the interdisciplinary processes generated and cross-related these results. The study explores the use of semantic graphs to capture those multidisciplinary data relationships. A minimum vocabulary has been created using just a few tens of concepts that has sufficient expressiveness to describe all the extracted "use cases", showing that the semantic graph method has the potential to describe a broad range of complex drilling related processes. The study also demonstrates that use of a parallel graph, employing other notions that do not expressly refer to the processes that generated the data can capture the description of how uncertainty propagates between each of those concepts. This paper describes the development of an initial reference implementation of semantic graph manipulation, the associated vocabulary and the description of uncertainty and quality notions and their linkage in terms of uncertainty propagation. This reference implementation will be available as open source to the industry drilling community allowing software solutions that capture and describe the generation of drilling data through multi-disciplinary workflows, and how they relate in terms of uncertainty propagation.

Due to the nature of the drilling process, there are several companies collecting data at the rig. Each company's data acquisition system applies its own time stamp to the data. Subsequent aggregation of data, for example in a data lake, relies on synchronized time stamps applied to the different data sources in order to collate the data. Unfortunately, synchronized time stamping is rarely true. This paper documents the different sources or errors in time stamping of data and provides some best practices to help mitigate some of these causes. There are many reasons for unsynchronized time stamping of data from different sources. It can be as simple as clock synchronization at the rig: each data providing or producing company has an independent clock. It can also be due to where the time stamp is applied: for example, at the data source or on data reception. Additionally, it can be due to how the time stamp is applied: at the start of the interval, the mid-point, or the end. Many of the protocols used at the well site have a high latency, mud pulse or electro-magnetic (EM) telemetry, or even WITS (Wellsite Information Transfer Standard), where the actual acquisition time may vary significantly from the time stamp. Perhaps finally, time stamping of derived data is always problematic given the unsynchronized nature of data sources. Synchronization of clocks within the data acquisition network is extremely important. The resolution of time synchronization depends on purpose: motion control for example demands high-resolution time keeping. However, for the purposes of local time stamping, synchronization to a Network Time Server with a resolution of one millisecond is sufficient. The issue is on agreeing on the common source, and agreeing on passage of the time signal through firewalls. Time stamping is a more involved matter, calling for agreement on standards and a degree of metadata transparency. The paper describes in some detail sender versus receiver time stamping, the downhole to surface time-stamp chain, and time stamping of derived data. Systems automation and interoperability at the rig site – allowing plug and play access to equipment and applications – rely on an agreed upon network synchronization scheme. Indeed, designing applications that must handle uncertain time adds considerable complexity and cost, not to mention the impact on reliability. This paper presents an ordered approach to a quite resolvable problem.

Thorough planning of drilling operations includes capturing best practices and expert knowledge as operational procedures to provide guidance during execution. However, due to the high level of uncertainty in drilling, and because conventional field procedures are generic and not context sensitive, they are open to interpretation. The resulting performance depends on the user's experience. In challenging situations, when crews are under time pressure and stress, any assistance with monitoring drilling conditions, identifying causes, and carrying out mitigation activities, is of value. This paper describes an advisory system to capture operational knowledge in the form of context sensitive procedures and rules. The system uses real-time measurements and connections to other systems to gather information and data on current operational conditions. Automatic execution of the procedures and rules is possible within the current level of process automation, but the manual use of the system can guide field users through complex tasks. This is particularly useful in handling manually complex operations within an automation scenario, and helps with maintaining situational awareness for operators. As an essential piece of the drilling automation package, the system monitors events created by various specialized processing and alarm-generating components, and triggers execution of required workflows. The deployment of the advisory system in the North Sea has focused on several aspects: drilling with RSS systems, automated trajectory drilling, borehole cleaning, vibration management, MWD/LWD tools troubleshooting, and drilling through stringers. For example, hard formation stringers recognized by the stringer-detection application, trigger execution of mitigation procedures, giving precise instructions depending on the wellbore inclination. The system easily integrates with, and controls, these application libraries allowing consistent execution promoting the reduction of NPT. Further, the advisory system allows autonomous monitoring of drilling events, and execution of best operating practices facilitates multi-well remote operations. Situational awareness is critical in situations where human operators and automated systems play inter-related roles. In such cases, it is important for users to know the state of each task and understand how activities they perform affect other users. The use of context sensitive procedures and rules, executed with connectivity to instrumentation, measurements and real-time applications, allows the user to maintain situational awareness while automating the drilling process.

Rotary steerable drilling systems are highly automated with inclination and azimuthal hold modes. These systems require only sparse communication downlinks to hold to a well plan or to compensate for geological "drift". Mud pulse transmission is one of the most complex communications technologies in any industry, yet measurement-while-drilling (MWD) companies have developed reliable bi-directional telemetry systems. The focus here has been on pulse generation and sophisticated decoding systems, able to detect signals, automatically, at a very low signal-to-noise ratio. Combining downhole automation and automatic mud pulse decoding with remote operations technologies delivers the technical-basis for unmanned MWD and directional drilling (DD) services. The infrastructure for unmanned services is a reliable surface communications network that connects the rig site with the remote operations center, and the realization that unmanned operations needs a new organizational structure for subject matter experts (SME's), in this case the MWD and DD engineers. This paper describes the now mature unmanned MWD and DD services (remote operations) in the US Land arena. These unconventional wells can reach in excess of a mile a day (so-called, MAD wells) with record footage in excess of 9,000 feet in a single 24-hour period. It will also describe the SME structure to deliver remote operations, and the key-role SMEs play in realizing unmanned operations. For example, moving directional drillers from the wellsite and into operator offices greatly improves decision-making. The paper will also examine the role of automation and digital technologies within this construct, especially how they will allow the unmanned model to migrate towards complex wells. Automated trajectory drilling systems, in which the rotary steerables can automatically correct for geologic drift, is the next step in downhole automation, soon to be followed by automated geosteering. Unmanned remote operations have only been around for a couple of years, but already operators are realizing efficiency and performance as they take advantage of a real-time digital infrastructure.

The drilling industry has substantially improved performance based on knowledge from physics-based, statistical, and empirical models of components and systems. However, most models and source code have been recreated multiple times, which requires significant effort and energy with little additional benefit or step-wise improvements. The authors propose that it is time to form a coalition of industry and academic leaders to support an open source effort for drilling, to encourage the reuse of continuously improving models and coding efforts. The vision for this guiding coalition is to 1) set up a repository for source code, data, benchmarks, and documentation, 2) encourage good coding practices, 3) review and comment on the models and data submitted, 4) test, use and improve the code, 5) propose and collect anonymized real data, 6) attract talent and support to the effort, and 7) mentor those getting started. Those interested to add their time and talent to the cause may publish their results through peer-reviewed literature. Several online meetings are planned to create this coalition, establish a charter, and layout the guiding principles. Multiple support avenues are proposed to sustain the effort such as: annual user group meetings, create a SPE Technical Section, and initiating a Joint Industry Program (JIP). The Open Porous Media Initiative is just one example of how this could be organized and maintained. As a starting point, this paper reviews existing published drilling models and highlights the similarities and differences for commonly used drillstring hydraulics, dynamics, directional, and bit-rock interaction models. The key requirements for re-usability of the models and code are: 1) The model itself must be available as open source, well documented with the objective and expected outcomes, include commented code, and shared in a publicly available repository which can be updated, 2) A user's guide must include how to run the core software, how to extend software capabilities, i.e., plug in new features or elements, 3) Include a "theory" manual to explain the fundamental principles, the base equations, any assumptions, and the known limitations, 4) Data examples and formatting requirements to cover a diversity of drilling operations, and 5) Test cases to benchmark the performance and output of different proposed models. In May 2018 at "The 4th International Colloquium on Non-linear dynamics and control of deep drilling systems," the keynote question was, "Is it time to start using open source models?" The answer is "yes". Modeling the drilling process is done to help drill a round, ledge free hole, without patterns, with minimum vibration, minimum unplanned dog legs, that reaches all geological targets, in one run per section, and in the least time possible. An open source repository for drilling will speed up the rate of learning and automation efforts to achieve this goal throughout the entire well execution workflow, including planning, BHA design, real-time operations, and post well analysis.

Abstract Drilling systems automation requires a downhole digital backbone for closed-loop control, as do many other real-time drilling, completion and production operations. The absence of a reliable, high data bandwidth, bi-directional communication method between surface and downhole is a barrier to digitalization and automation of the oil field. This paper describes the development and successful drilling field trial of a micro-repeater wired pipe – effectively "smart pipe" – that removes this barrier. The developed system uses battery-powered micro-repeaters (a fail-safe signal booster) placed within the box of each tubular and fully encapsulated dual RF-resonant antennas to transmit data between tubulars. The current system delivers 1-Mbps backbone data rate with a maximum payload of 720 kbps, and with a very low latency of 15 μsec/km, making it ideal for control-loop applications. The system design focusses on reliability: failure of multiple components will not affect telemetry. The prototype system has been rigorously field tested during drilling in Oklahoma. Testing occurred on a drilling rig in Beggs, Oklahoma. The first trial (2016) covered drilling operations, the second (2017) covered controlling downhole technology; both were successful. The drilling trial demonstrated fitting the system to pipe with conventional API connections, standard rig-floor pipe handling, reliable wireless transmission between surface receivers and wired pipe network, the use of multiple along-string measurements of temperature and vibration, and simulated component failure. Of particular note was the surface system: it is wireless and no modification to the drilling rig was required. Conventional tubulars can be refit with the system, which removes a barrier to the use of wired pipe for automation and LWD/MWD measurements in lower cost onshore operations. There is a benefit for drilling operations: all pipe joints contain a micro-repeater and are addressable for "smart pipe" applications such as an electronic pipe tally, and pipe condition monitoring. Drilling operations are the first users of the system, but it serves other operations, for example tubing conveyed wireline operations. The smart wired pipe concept is truly innovative. It enables drilling systems automation and logging-while-drilling applications, such as seismic-while-drilling with along-string sensors, by providing a fully open acquisition and control platform to the industry.

Abstract Downhole tools in bottom-hole assemblies are subject to high dynamic loads during drilling operations. The negative impacts of these dynamic loads can be inefficient drilling with low rate of penetration (ROP), reduced downhole directional and formation measurement service quality, and downhole tool failures with associated non-productive time. The dynamic phenomena can be categorized by direction into axial, torsional and lateral vibrations, and by excitation mechanism into forced excitation, self-excitation, and parameter excitation. Forced vibrations are mainly caused by imbalances in the drilling system or by the working principles of downhole tools such as the mud motor. Self-excitation mechanisms are mostly driven by the interaction of the bit, reamer or drilling system with the formation, and can cause detrimental dynamic behavior such as torsional stick-slip, bit bounce, and backward whirl. These diverse vibration phenomena require tailored mitigation strategies. To a certain extent, these mitigation strategies are contradictory. Misinterpretation of downhole measurements can lead to even worse vibration levels with severe consequences for reliability, ROP, and measurement quality. As a consequence, downhole measurement devices should differentiate vibration phenomena. This distinct differentiation could then be used to choose appropriate mitigation strategies. This paper analyses and defines the requirements for dynamics measurement devices. The specification, number, and placement of sensors and their associated sampling rates are examined to distinguish vibration directions and phenomena. The usefulness of these requirements is demonstrated using examples of torsional stick-slip and high-frequency torsional oscillations, lateral vibrations, and backward whirl. The results of kinematic modeling are analyzed and compared to high-speed vibration data from field runs measured with the latest generation of vibration measurement tools. Possible misinterpretation of vibration conditions in the case of inappropriate measurements is shown. The results are discussed by comparing theoretical modeling with field data. The defined requirements and guidelines enable a flawless interpretation of downhole vibration measurements and unveil drilling optimization opportunities. Different vibration phenomena can be identified reliably and appropriate mitigation strategies applied in real time at the wellsite by the driller or automation systems. This finally reduces the vibration load on the drilling system, increasing its reliability and performance.