Recent events and studies show that wellbore stability and geopressure events continue to plague the oil industry with issues that affect the safety of people and the environment. In addition, events such as kicks and lost circulation also create significant loss of time and productivity, commonly referred to as non-productive time (NPT). Deepwater studies have shown NPT related to kicks, and lost circulation can amount to 4.5% of the total well construction time. Consequently, early kick and lost circulation detection is crucial to eliminating detrimental effects on human and environmental safety, in addition to minimizing NPT.Kicks frequently happen during connections, and flowback fingerprint monitoring has been used for more than a decade across the industry to aid in kick detection. However, setting alarm thresholds and identifying abnormal flowbacks has been a manual process that relies heavily on the experience and intuition of the engineer who performs this critical safety monitoring. This manual process frequently misses early signs of influx, and thus greatly increases the well remediation time .This publication focuses on the development and deployment of an automated flowback monitoring technology. The new solution aids drillers and drilling engineers by generating intelligent alarms relevant to current well conditions for early kick and loss detection, which can result in detecting kicks up to one connection earlier than the existing manual method. This paper demonstrates the benefits of Smart Flowback Fingerprinting over existing practices and how it can significantly reduce safety risks and NPT.

Our industry has been advancing drilling automation concepts to increase safety, reduce drilling risk, and improve the overall repeatability of the drilling process. At the same time, increased drilling costs, available expertise shortage, and safety-related issues with personnel at the wellsite, have prompted the need to provide interpretation and advice remotely.Remote centers enable subject matter experts (SMEs) to work on multiple, geographically dispersed wellsite operations concurrently without having to be on location. These centers facilitate the ability of multiple experts to assemble quickly and collaborate to solve complex challenges without adding the HS&E risk of additional personnel at the wellsite. However, the increased volume of information available from technologies like wired-pipe, combined with the shortage of experienced SMEs to quickly interpret datasets, create new challenges.Digital oilfield applications have challenged operators and service providers to leverage remote capabilities to aggregate huge data volumes and provide expert knowledge for multiple operations. Focusing attention of personnel on the most important information to make accurate and timely decisions requires new techniques. New systems require automation so that risk recognition and advice can be automatically delivered to the right experts to streamline while-drilling decision-making.New cased-based reasoning technologies can compare the current drilling situation to similar previous case histories where problems occurred. This real-time decision automation enables identification of similar events that led to drilling problems on similar wells drilled in the past. From those historical cases, similar solutions are presented to avoid potential drilling problems before they occur. This while-drilling response provides the automated real-time connection between previous experiences and current operations that reduce drilling risk and ensure greater repeatability.A case history is presented on the use of case-based reasoning to enhance automated advice by identifying hazardous situations in advance that enabled successful corrective action implementation.

Due to the increased complexity of oil and gas wells being drilled worldwide, many encounter significant geologic uncertainty during wellbore construction. This uncertainty can be manifested in the form of unplanned drilling events such as kicks, lost circulation, and borehole stability problems. These issues expose operators to serious health, safety and environmental (HSE) risks and frequently lead to significant cost overruns. However, if a more proactive effort was placed on predicting and identifying trouble zones in real time, it may be possible to achieve a substantial reduction in nonproductive time (NPT) while simultaneously reducing safety risks.With the abundance of drilling and logging data now available in real time, much of this information can be used to recognize potential problem areas while drilling. Until recently, ensuring that the right person receives the right information at the right time to make an informed decision has been accomplished primarily through manual processes. However, computer systems available today can automate the filtering and distribution of relevant information to personnel quickly for more rapid assessment of the current situation.In addition to data handling and distribution, these systems can also provide automated alarms that aid in reducing the probability of an unplanned event by focusing the attention of personnel on the most important datasets as well as their interpreted relationships. Alarms can automatically interpret complex real-time datasets in the context of pre-well models and quickly provide warnings without relying solely on the experience of the individual to decipher the information in context and detect potential danger. They also provide assistance for less experienced personnel enabling better risk assessment awareness and, in turn, more reliable decision making.This paper presents a case study for a system of real-time automated wellbore stability alarms aimed at identifying trouble zones and improving ahead of the bit predictions. The goal was to reduce the potential for kicks, lost circulation and wellbore instability events. Special software is used for processing real-time data, in the context of geomechanical modeling, and raising appropriate alarms when conditions cause drilling parameters to deviate outside an acceptable level. The case study also discusses how these alarms can be used in a remote services platform by transmitting real-time information from the wellsite to experts in town, which enhances collaboration and improves customer value through better engagement. Finally, the paper describes how automated alarms help to establish and enforce better communication protocols and accountability through an acknowledgment process that includes depth and time-based comments.

The oil and gas industry has increased its efforts in preventing and mitigating risks associated with loss of well control and loss of primary containment. Initiatives have primarily focused on increasing the awareness and education of field and operations personnel. The foundation of this strategy rests on the belief that increased awareness of threats, risks and enhanced training will lower well control risks and eliminate well control events. Despite this renewed focus, industry data show that well control events and high-potential near-misses have not diminished. In addition, findings from incident investigations point to human-factor-related causes, including lack of procedural discipline, non-compliance errors and cognitive errors. For organizations to deliver flawless execution at the wellsite while effectively preventing or mitigating well control or loss of primary containment events, a robust closed-loop methodology that leverages smart risk detection and mitigation systems must be employed. This paper analyzes the critical process safety requirements in the industry and provides solutions centered on a smart integrated digital platform. This platform, built on technologies such as precursor sensor and alarming technologies, barrier and equipment health monitoring, wellsite performance analysis, sophisticated workflow management and video and audio analytics, will effectively coordinate and manage these dynamic risks. A data management workflow that uses automated risk assessments, threat detection, structured and nonstructured contextual data will minimize the impacts of human factors and drive operational efficiency, process assurance, reduction in nonproductive time and project cost. These enhancements will enable global organizations to proactively drive effective risk management of well control and loss of primary containment events. This paper will explore the application, methodology and value of this smart, integrated digital platform through the presentation of case studies.