Drilling automation is gaining momentum as seen by the increasing participation at workshops. Most of the early efforts of drilling automation have focused on incorporating specialized algorithms by both oil companies and service companies that try to improve the drilling performance. As drilling automation efforts try to scale to all rig types and all drilling operations there will be an increasing need to integrate and connect equipment from different vendors with different control systems to create complete automated work flow. Implementing drilling automation on existing rigs requires integration of equipment owned by the drilling contractor, service company and even the operator. This paper will investigate different architectures that have been successfully used in other domains to solve this key technology challenge to support Automation on a drilling rig. Cloud computing has been used as a way to provision services on demand. There are several public cloud services that are available via the internet. The focus of this paper will be the implementation of a community cloud infrastructure that can be implemented on a drilling rig, provisioning various services key to the successful implementation of drilling automation. IntroductionThe focus of this paper will be on land rigs where there has been more efforts to advance drilling automation in recent years. Traditionally Land rigs have not seen much effort on automation due to the emphasis on low cost drilling. Availability of local crew and the lower cost of managing crews on land has not been conducive to investments in automation. Most of the rigs build in the past have been generic rigs with a mast, sub-structure, drawworks, rotary table, pumps, fluid management and even a topdrive was an addon. All additional technology required to drill wells were added via service companies. Most service companies bring in enough equipment to be self sufficient and do not rely on the rig equipment. This philosophy has forced each service company to add an independent rig network, satellite connection, additional displays on the rig floor. The landscape has been slowly changing to where more sophisticated wells are being drilled, often in remote areas, requiring more of the services as standard packages with an increased emphasis on safety and reduction of personnel on the rig floor. Most of the land rigs being built today are PLC controlled rigs with integrated drawworks, topdrive and pumps. The ability for the service companies and operators to tie into these automated rigs and create automated workflows is there, but there are many challenges to making this happen including lack of interface standards and viable IT infrastructures. As we try to understand how to integrate equipment control systems from different service companies, there has been a need to look at how IT infrastructure solutions from the general IT industry can help bridge some of these gaps. Before we look at how IT infrastructure can help solve some these challenges, we need to look at the differen...
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.
Although mud pumps are critical rig equipment, their health monitoring currently still relies on human observation. This approach often fails to detect pump damage at an early stage, resulting in non-productive time (NPT) and increased well construction cost when pumps go down unexpectedly and catastrophically. Automated approaches to condition-based maintenance (CBM) of mud pumps to date have failed due to the lack of a generalized solution applicable to any pump type and/or operating conditions. This paper presents a field-validated generally applicable solution to mud pump CBM. Field tests were conducted during drilling operations in West Texas and Japan, to verify the feasibility of the developed pump CBM solution. An accelerometer and acoustic emission (AE) sensor were attached to pump modules, and data was collected during drilling operations. Anomaly detection deep-learning (DL) models were trained during run-time to pinpoint any abnormal behavior by the pump and its elements. The models were trained only with normal state data, and a damage score characterizing the extent of damage to the mud pump was calculated to identify the earliest signs of damage. The system correctly identifies the degradation of the pump and produces alerts to notify the rig crew of the damage level of key mud pump components. During the field tests, different hyper-parameters and features were compared to identify the most effective ones for identifying damage while at the same time delivering low false positive rates (i.e., false alarms during normal state pump operation). The developed CBM system thus provides a generalized solution for pump monitoring, capable of working for different pumps and different operating conditions, and only requires several hours of normal state data with no prior pump data information. This system eliminates the environmental, health and safety (EHS) concerns that can occur during human-based observations of mud pump health, and avoids unnecessary NPT associated with catastrophic pump failures. The final version of this system is expected to be a fully self-contained magnetically attachable box containing sensors and processor, generating simple indicators for recommending pro-active pump maintenance tasks when needed. This is the first successful attempt to validate a universally applicable DL-based CBM system for mud pumps in the field. The system allows more reliable continuous and automated pump monitoring by detecting damage in real-time, thereby enabling timely and pro-active mud pump maintenance and NPT avoidance.
Summary Due to the nature of drilling operations, there are several companies collecting data at the rig. The data acquisition system of each company 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.
Operators, service providers, and contractors are improving the well construction system using transformative digital technologies across multiple companies and within levels of disparate organizations. Well plans are managed in a new way with the creation,execution and continuous improvement of processes to materially deliver value from the onset. A typical operator’s well planning process involves developing a well program across multiple internal and external entities in the design and plan stages while various requirements and details are considered and confirmed. As the well gets closer to being drilled, the well plan must also consider the capabilities and technologies supported by the service companies and drilling contractor.This people-based process leads to variability in assumptions and objectives in standardized plans which can result in execution risk and variations in safety, quality, delivery, and cost (SQDC) performance. One major variability source occurs because of the disconnected nature in which people move data between one another in well planning documents and then to field operations. The authors propose a new way to manage this process across organization and discipline boundaries to reduce this variability. This paper details how digital technologies were incorporated into an agile pilot program to create a common framework for the exchange and management of the well plan. It follows the plan from its development to implementation in a unified, seamless process. Further the authors will demonstrate how the solution was developed across multiple organizations to deliver material value to all parties. Three companies created a collaborative business model to deliver a new digital system enhancing well construction planning and execution in terms of speed and completeness of data transfer, ease of access,and availability for building new data-based workflows and reporting. This business model continues to drive alignment throughout the companies’ well delivery business functions. This paper illustrates, "What is the value that digital transformation brings to my organization or job function".The simple answer is that if executed effectively, the transformation should produce a material improvement or outcome for the business. Digital technology creates material business value as a project management tool directly coupled with wellsite technologies. This approach, in turn, will enable improvement of the well design and SQDC performance in the delivery of the well plan.
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