This paper presents results and knowledge shared from the SPE Applied Technology Workshop held in Vail, Colorado USA in July 2012 titled Well Construction Automation -Preparing for the Big Jump Forward.Automation in drilling and completion operations is coming quickly, and its rapid adoption will leave many industry players behind if they are not aware of the future it will bring. Advances in control and automation of the whole drilling and completion process will increase improvements in safety, performance, quality, reliability, consistency and interoperability. This progressive application of automation will also create shifts in skills and competencies, and transform the role of the driller, rig crew, and service specialists along the way. Advances in automation are being made on multiple fronts, and many lessons are available from its adoption in other industries and the transformation industrial automation afforded in the 1990s.This workshop included important lessons learned from other industries and provided an update on the latest advances in automation developments. It explored the applications of such technologies as robotics, machine learning, and autonomous task performance without continuous human guidance, along with the speed with which these technologies can be applied.This was the first workshop that has actively brought people involved in automation from other industries into the discussion on drilling systems automation. The workshop involved key speakers and participants from leading edge applications including academia and the Defense Advanced Research Projects Agency (DARPA). These organizations are automating things that the drilling industry has not yet heard about. The workshop participants developed plans for adopting these technologies into drilling systems and created a vision of rapid automation adoption into drilling operations. Workshop SummaryThe workshop was attended by 120 participants that included a broad cross section of experts connected to automation inside and outside oil and gas (Fig 1).The business case for automation, highlighted by drilling industry practitioners, was the improvements in safety, drilling performance, and consistent / predictable drilling operations. It is anticipated that automation can solve the current situation whereby the driller is overloaded with inputs and tasks. Successful automation projects will require a multi-skilled team that includes well engineering, process automation control / optimization and information technology.It is anticipated that systems integration will enable interoperability (a.k.a. plug and play) between downhole and surface tools and machinery from different Original Equipment Manufacturers (OEM's) and that operators will begin to specify automation and adherence to specific communication and interoperability protocols in their contracting documents, but there is a significant division around the need to implement standards for interoperability. Essentially, standards were the The workshop participants collectively agreed...

Multiple literature studies have indicated that a significant amount of data collected during drilling operations is unreliable. To move towards better data quality, two critical hurdles need to be overcome. First, the case for the value of good data needs to be made, so that resources can be allocated towards improving data quality. Second, a process needs to be established within the operator company to measure and improve the quality of data. This paper is a case study in addressing these challenges. In this work, we focus on eight core surface sensor measurements essential to drilling operations (block position, hook load, rotary speed, rotary torque, pump strokes per minute, flow rate out, standpipe pressure and pit volume) and attempt to assess/improve their quality. The first step involves identifying how much of each measured data deviates from their accepted values. This is most economically accomplished using automated data validation software. Once the root cause is identified, steps can be taken to rectify the problem. Four rigs in North America were identified for this trial conducted over a six-month period. The goal is to establish a data quality improvement loop that continually accesses data, identifies issues, and implements corrective actions. This paper explains this process and how it has been applied to improve the quality of drilling data.

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.

Management - No abstract available.

The use of real-time mechanics and dynamics measurements has expanded and evolved significantly in the last thirty years, enabling the safe and reliable drilling of complex wells. Unfortunately, these measurements are poorly defined, which inhibits straightforward analysis and hinders development of models, simulators and automation applications. Adoption of open measurement guidelines will provide the transparency required to improve the value of existing measurements and accelerate the development and adoption of enhanced applications and automation. Mechanics and dynamics measurements currently can be processed and transmitted from downhole in near real-time and then merged with other surface measurements. Optimization of the drilling process uses these combined measurements for monitoring, advising and control, thereby delivering a quality borehole as planned, in a safe and reliable fashion. Processing includes real-time filtering, mathematical modeling, and simulation, which are significant parts of any systems approach. There are currently multiple methods used to quantify downhole and surface vibrations, displacements, and forces. Tools use dissimilar sensor and processing configurations, so they yield significantly different data. Regrettably, these different measurements are given the same name, and published descriptions often do not include the details required to understand and properly utilize the data. Examples of key information include sensor characteristics, sensor arrangement, bandwidth, and signal processing. Guidelines are suggested to resolve this state of confusion, including: terminology describing each drilling mechanics and dynamics sensor, configuration, and measurement; and metadata describing how a data value is derived, filtered, processed, transmitted and stored. This paper examines and defines drilling mechanics and dynamics measurements. It recommends measurement practices and good processing techniques of these measurements, with examples, and presents a recommended open measurement framework. Adoption of this framework will resolve the current state of confusion and uncertainty, enable all parties to develop monitoring, advising and control applications that use these data, and help lower well costs and improve borehole quality.