By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Cookie Policy and Privacy Policy for more information.

What You Need to Know About Digital Wells and Field Optimization

What You Need to Know About Digital Wells and Field Optimization

Today’s oil and gas production manager faces many challenges ensuring wells are producing optimally. Managing more wells with the same (or fewer) resources than before, is forcing them to make a Faustian bargain; focus their precious time on the highest producing assets, while neglecting everything else.

Today’s oil and gas production manager faces a myriad of challenges in ensuring that wells are consistently producing at their highest possible potential. The advent of shale, and its high-volume drilling process, means that onshore production engineers are managing many more producing wells than they have in years past. This requires production engineers to optimize more wells with the same (or fewer) resources at their disposal. As a result, production operation managers have been forced to make a Faustian bargain; to focus their precious time on the highest producing assets, while neglecting everything else.

Industry data reflects this operational reality. As few as 20% of existing wells are actively managed by production professionals, while as many as 80% go un-managed and are therefore sub-optimized. In a recent discussion, one large operator noted that as many as 30% of all gas wells can be non-producing on a single day due to hydraulic power units falling over, solids eroding well necks and other issues. As a result, the vast majority of oil and gas wells are producing well below their potential.


To complicate this gargantuan task, unconventional wells experience dynamically changing production profiles for oil, gas, and produced water over the course of their producing life. In fact, production can fluctuate quite significantly even on a daily basis.  Therefore, optimizing an unconventional well requires high-fidelity, real-time production data in order to make operational decisions on key well parameters, such as choke settings and artificial lift pump set points.

Legacy well management systems struggle with keeping unconventional wells optimized given these dynamic production parameters. They are good at providing remote control, but they aren’t “smart”. As a result, keeping a well producing at its highest potential generally requires frequent manual intervention by a production engineer. In order to determine the appropriate intervention, the production engineer needs actionable real-time data for each individual well, and visibility across the entire portfolio of producing assets that she’s managing.


Within every operational challenge lies a business opportunity. Internet of things (IoT), machine learning, cloud computing, and edge analytics are uniquely positioned to enable innovative solutions for dynamic optimization problems. In fact, these digital technologies are making the job of the production engineer much easier, by giving them visibility across all of their producing assets and providing them with actionable insights to make faster and better-informed decisions. In addition, well pad design and performance simulations rarely match reality. There are too many uncertainties relating to surrounding systems and the day-to-day characteristic of the well. In order to optimize operational parameters, production managers need a system which allows them to investigate real-time performance output against statistical data. This includes all telemetry on the well site, including flow measurements.


One such digital oilfield technology is virtual multiphase flow metering (VFM). VFM leverages the existing well telemetry, such as pressure and temperature readings upstream and downstream of the choke. These measurements can be read and analyzed in real-time using a well-proven physics-based model that leverages a differential equation. Machine learning can then be applied to provide greater certainty into the accuracy of the physics-based model, to determine optimal well choke and pump settings, and to continuously calibrate the VFM model and monitor the calibration of the underlying sensor measurements.

VFM can provide equivalent, and often, more accurate flow measurement when compared to a physical multiphase flow meter. When paired with an IoT gateway computer at the well-pad, the VFM doesn’t require the installation of expensive physical flow meter hardware. Real-time well measurements can be streamed to the cloud for processing or they can be processed locally on the edge device, depending on the use case. This opens up the possibility of scaling a VFM system across hundreds, if not thousands of wells, at a fraction of the time and cost of a physical flow meter.

Because VFM is highly scalable, it can enable accurate real-time, well-bore level production data upon which advanced machine learning enabled production optimization applications can be built.  McKinsey & Company estimates that there is a 10% oil and gas production improvement opportunity globally, which represents an enormous unrealized economic potential. Technologies such as VFM hold significant promise to empower data-driven and machine learning-enabled production optimization solutions that can capture this tremendous opportunity. This is a potential game-changer for the oil and gas industry.