Leveraging oil and gas digitalization to emerge from the pandemic

The oil and gas industry has faced numerous challenges in the recent past. Even though the price of oil is improving, there remain significant fluctuations, leading to a lot of uncertainty and causing our industry to continue struggling with macro issues. The global pandemic, which has persisted into 2021, has magnified the struggles. As a direct consequence, we are seeing unprecedented shortages in the labor market and delays in the global supply chain that are not expected to improve until the end of 2022. Both have resulted in sharp rises in costs that are further delaying the recovery of balance sheets, despite increasing oil prices.

CONSTRAINTS ON CAPITAL AND LABOR FORCE

Fig. 1. Operators are adjusting their operating philosophies to include a renewed focus on production optimization to maximize their existing assets.

In addition to the external challenges that companies increasingly face, capital expenditures have been slashed anywhere between 20% and 30%. Companies across the board are making tough decisions on where to invest or not to invest. In such an environment, stakeholders are also looking for ways to maximize their capital investments, already made in prior years. Another significant resource crunch that remains a mainstay of the industry is the severely reduced workforce. This means there aren’t enough experienced personnel available to fulfill current needs, and the employees that are on staff are challenged to address the ever-increasing workload. This means we must continue to be more efficient in our operations, despite improvements in the market conditions, Fig. 1.

To address the numerous challenges, companies are adjusting their operating philosophies. For example, there is a renewed focus on production optimization. The production phase requires relatively low capital investment, yet it has a significantly high return on that investment. According to McKinsey & Co, production management projects deliver a much larger economic return than new well drilling in all type-curve areas and requires a smaller capital expenditure. Therefore, companies are increasingly focusing their operational strategies toward maximizing investments by boosting production, increasing uptime, and enhancing the personnel efficiencies relating to operating the wells.

This means there are tens-of-thousands of gas-lifted wells in North America, alone, that stand to benefit from this type of investment application. Globally today, most wells are already on some form of artificial lift. However, managing these wells can be more of an art than science, which is compounded by changing production conditions and situations. This in turn, means that sub-optimum operating conditions, such as over-injection and under-injection scenarios in the case of gas-lifted wells, are common. This, then, translates to the fact that production is either left on the table, or an operators’ lift-gas capacity is misallocated or underused.

DIGITAL TECHNOLOGIES INCREASE PRODUCTION EFFICIENCY

Fig. 2. With the launch of Industry 4.0 technologies, solution providers are introducing additional sensors or controllers that would need to replace the existing hardware before benefits of the technology can be realized at the wellsite.

At Weatherford, we have experienced a renewed interest from clients interested in the latest digital offerings, especially because of their capabilities for remote operating, visualization, Edge automation, and artificial intelligence. With the Industry 4.0 technologies coming into the oil field, solution providers are introducing additional sensors or controllers that would need to replace the existing controllers before the benefits of the technology can be realized at the wellsite, Fig. 2.

For example, companies are developing and introducing additional sensors to measure pressure, temperature, vibrations and flowrates, either in the subsurface regimes or at the wellhead or near the wellhead. The solutions involve transmitting large data sets via IoT gateways and hubs to the Cloud, where data scientists build AI-based, machine-learning models. This requires a significant investment in capital, which as described earlier, is severely constrained now. Additionally, many of these technologies are still in their infancy, with limited or no prior history of success. These circumstances are significantly increasing the risk of the technologies failing to achieve the stated objectives.

A significant percentage of artificially lifted wells already have sensors and automation equipment at the wellsite that were installed over the last couple of decades or earlier. However, the problem is that the controllers only perform basic control functions, whereby they have just enough computational capabilities to perform the simple functions that operate the artificial lift equipment at pre-set operating setpoints—specified by the operator or the production engineer at the wellsite using a SCADA software solution.

Due to limitations of communication bandwidth and availability of qualified personnel, these operating set points are updated infrequently. As a result, oftentimes the controller is managing the well with outdated setpoints, even when reservoir conditions and other physical conditions change. Knowing that this is highly likely, when an operator or the engineer sets the set points, they set it to safe limits, to allow for enough tolerance to account for such changes.

The Weatherford digital solution uniquely addresses these challenges at the wellsite. It maximizes the use of existing digital equipment at the wellsite and integrates proven lift optimization and well-modeling technologies into the Edge. ForeSite Edge is a smart device that can be implemented within the enclosure of any controller already at the wellsite, or it can be installed as a stand-alone device on new wells with no controllers. The result is the same. It introduces not only proven models to the well, but also advanced artificial-intelligence capabilities to achieve higher production and lower operating expense.

COMMINGLING INCREMENTAL GAINS

Fig. 3. Wells on artificial lift, with unique operating conditions, can now be autonomously controlled achieving operational improvements that reduce OPEX by up to 78%.

Through use of physics-based modelling at the Edge, it is now possible to continuously and autonomously optimize an artificially lifted well that requires minimal human intervention, Fig. 3. The Edge device is an IoT-enabled smart device that can analyze the operating conditions of the well and continuously adjust it to match the changing inflow conditions. Edge also will notify an operator instantly when optimum operating conditions can no longer be maintained autonomously and subsequently require intervention from a human expert.

In short, a smart device-controlled, artificially lifted well can, and will, run itself in optimal conditions by altering set points within defined technical limits. This has been the promise in the industry, but the reality hasn’t always met the promise. ForeSite Edge devices, currently running on real artificially lifted wells with different operating conditions and optimization needs, are autonomously controlling the wells and achieving operational improvements. These improvements reduce OPEX 78%, increase production between 5% and 15%, and maintain other operating KPIs, as set by the operator.

It is important to remember that traditional controllers have limited storage capacity. Therefore, they are optimized to store high-frequency data only for the most recent period, going back just a few days and recording only significant events for longer durations. For example, rod-lift controllers store about 1,000 dynamometer cards. This means that for a given pump-off cycle, it only stores the full- or start-up card and the pump-off or shut-down card. None of the cards in between are available, unless the SCADA system requests a card as part of its scheduled-polling or on-demand from a user.

Therefore, when a failure occurs, it is highly unlikely that sufficient data are available to analyze the root cause of failure. On the other hand, ForeSite Edge has storage for over 3.5 million dynacards. This means that it has cards from every stroke for over a year on a unit running 6 SPM. The benefit here is clear. When a failure occurs, dynacards for every stroke prior to the failure are available for analysis, helping diagnose the root cause that led to the failure.

IOT-BASED OPTIMIZATION AND CONTROL

Fig. 4. Since ForeSite Edge uses IoT-based MQTT protocol to communicate, it can send notifications instantly when it identifies potentially troublesome events by running continuous lift-optimization analysis.

The availability of high-frequency data means that it is now possible to analyze lift performance continuously for a better understand of well behavior, including minor shifts in lift performance. However, analyzing the data requires a user to run lift-optimization software on their desktops. This poses two challenges: 1) it requires all the data to be transmitted from the Edge to the location where the lift optimization software is running; and 2) it requires significant investment of time by an engineer to analyze the data before making decisions, based on analysis. Instead, ForeSite Edge continuously runs proven well-modeling and lift-optimization software at the wellsite, thereby reducing the need to transmit large volumes of data and the required engineering time through automated analysis, Fig. 4.

Since ForeSite Edge uses IoT-based MQTT protocol to communicate, unlike traditional controllers that rely on polling-based SCADA systems, it can send notifications instantly when it identifies any notification-worthy events by running continuous lift-optimization analysis. Since the device communicates with SCADA systems, such as the CygNet IoT platform, operating within an operator’s own premises or in the Cloud, the data are guaranteed to be available when needed. This allows an engineer to focus on higher-value activities and perform a deeper analysis of the well, only when notifications are received. Also, running analysis continuously on the Edge and notifying instantly when anomalies are detected helps reduce failures between 5% and 30%.

Based on the analysis that it performs using either its physics-based model, AI-based model, or combination of both, ForeSite Edge also has the intelligence to autonomously optimize the lift system. And since ForeSite Edge connects with any manufacturer’s device, it can do this by adjusting the controller set points, regardless of the controller at the wellsite. A powerful feature of ForeSite Edge is the ability to create custom autonomous-optimization algorithms. Companies have made use of this ability to create algorithms to achieve their specific optimization objectives. Companies also have developed algorithms, based on their ESG objectives to reduce their carbon footprints. They have done this by having the Edge device continuously adjust run time of the artificial lift equipment to balance production and not just against power consumption.

VALUE DELIVERED

An example of the benefits of ForeSite Edge in action comes from a North Dakota oilfield operator, where ForeSite Edge boosted production while reducing wellsite visits 70%. ForeSite Edge delivered autonomous control to two rod lift wells. High-frequency data enabled production-uplift recommendations that increased output and efficiencies valued at $40,000 per year, including maintaining pump fillage above 70%. The autonomous control capability increased production through continuous optimization in two reciprocating-rod lift wells, driven by long-stroke pumping units, equipped with variable-speed drives (VSDs).

Many wells change artificial lift systems more than once during their life span. Traditionally, each of these modifications calls for a new wellsite controller requiring extra capital investment each time. ForeSite Edge technology has built-in controls for a well, regardless of lift type. This means a change in ALS at the wellsite will not require a change in the controller, ensuring that not only does it provide faster ROI, its value grows faster throughout the life of the well.

ForeSite Edge allows end-users to monitor not only the health of their artificial lift equipment but also the health of the device itself. Operators can plan their wellsite visit, based purely on monitoring all the devices in the field remotely to manage their daily route in the most efficient manner possible. Reducing wellsite visits has the multi-faceted benefit of fewer miles driven, reduced carbon footprint, and improved HSE at the wellsite. Remote management of wells and their connected devices is a key driver for maintaining wellsite automation.

IoT-based technology. Industry 4.0 technologies are bringing digital transformation, not only at the wellsite but elsewhere within organizations and is revolutionizing traditional systems that companies have come to rely on, such as for SCADA with production and drilling optimization. The purpose of SCADA has always been to acquire data from simple oilfield sensors and controller devices and remotely control them. It relies on traditional communication protocols by polling for data from each device at a scheduled interval. CygNet, the long-standing SCADA system from Weatherford, is used widely by operating companies in the oil field.

Companies can now leverage their prior investments in SCADA, using an IoT platform so that it can communicate with existing equipment as well as smart devices that publish data instantaneously through IoT-based communication protocols. Additionally, significant advances are occurring in how operators and engineers view these data. Instead of accessing the data on an HMI that is installed on a desktop, thus making it cumbersome to maintain and also tethering the operator to a single device, a thin client version of the HMI on the web now enables access to the data on any device types, including desktops, laptops, tablets and smart phones.

Production performance strategies no longer focus exclusively on lift optimization or reservoir simulations performed by specific teams in silos. Instead, this includes a complex set of integrated and interconnected workflows and decision-making by multiple teams that are able to coordinate their activities to generate maximum value from the reservoir to the surface network and every corner of the asset. Recognizing the need for a digital solution to continuously perfect production strategies in an ever-changing environment, Weatherford’s ForeSite production-optimization platform delivers real-time, exception-based surveillance and asset-optimization using well modeling, surface-network modeling, and dynamic reservoir-integration. This includes the business-process workflow for quarterly planning that incorporates technical constraints as well as corporate KPIs.

Leveraging the high-frequency data transmitted from devices on the Edge, such as ForeSite Edge, provides an ever-increasing quality of information, using advanced analytics and physics-based models. ForeSite helps prioritize uplift opportunities, identify bottlenecks, and detect failures before they happen. Over the long run, this helps increase production potential, optimize lift, improve well and asset performance, and integrate all in-house systems to maximize current technology investment.

Middle East. A major operator in the Middle East installed an asset-wide optimization platform that enables real-time data monitoring, production optimization, and accurate production allocation. The 3,000-well asset currently produces 1.6 MMbopd and is primarily natural drive, but declining. The current operating regime features some artificial lift, as well as measures to maintain reservoir pressure, including 220 ESPs (electric submersible pumps), 250 gas lift systems, and 500 gas and water injection wells.

The optimization platform enabled a cultural shift around production optimization and efficient workflows. The platform enabled the operator to increase production 11%. The production optimization platform enhanced personnel efficiency by enabling a collaborative optimization environment that unified work across all wells, the surface network, and reservoir. The facilitated enterprise-level data tracking and management system established a consistency between well and network models.

South America. A conventional well operator in Colombia successfully leveraged the ForeSite production optimization platform to avoid a 14,500-bopd loss in production while enabling remote restarts for 850 ESP wells in minutes, following blackouts caused by seasonal rains. Intuitive ForeSite processes and controls enabled the operator to reduce their per-well recovery time from three minutes on the first day to just 56 seconds by day three. All well-recovery operations were Covid-19 compliant, with no more than three operators needed to remotely restart approximately 850 wells.

PATH FORWARD

The hallmark of Industry 4.0 is the ongoing combination of traditional manufacturing and industrial practices with the latest smart technology. This primarily focuses on the use of large-scale, machine-to-machine communication and IoT deployments to provide increased automation, improved communication, and self-monitoring. This includes smart machines that can analyze and diagnose issues without the need for human intervention. The introduction of these concepts enables companies to predict equipment failures, manage wells without human intervention and manage an asset by exception, meaning that personnel can focus on what matters most on any given day.

About the Authors

Bimal Venkatesh

Weatherford

Bimal Venkatesh is head of product management for production automation and software at Weatherford. With over 20 years of oil and gas experience, he has implemented the company’s vision and strategy of Industry 4.0, building production-optimization software, using advanced analytics and developing a digital portfolio of production automation and software products. Mr. Venkatesh holds an MBA from The University of Texas (Austin), an MS degree in chemical engineering from University of Wyoming, and a BS degree in chemical engineering from Indian Institute of Technology, Madras.