Well Portfolio Optimization: Reduce cost and time by over 85%

 

 

The challenge

Asset teams are tasked with proposing well production enhancement opportunities on a regular basis. While this process is crucial to sustaining asset production, the identification of potential workover candidates is limited by budgetary constraints and is historically a lengthy, manual process, which consumes several weeks of engineers’ time in every iteration. Moreover, best practices and lessons learned from past interventions are rarely captured, this prevents systematic improvement of the candidate selection methodology and choice of intervention.

The solution

Using artificial intelligence (AI) integrated with a production operations solution, a standardized and automated approach to rapidly screen and rank large well count assets (hundreds or thousands of wells) in a fraction of the time and repeatably, was proposed. The solution enabled the proactive management of existing wells, by keeping the production enhancement opportunity pipeline full and expediting potential candidates through the opportunity maturation process (OMP) in an integrated and collaborative framework. Engineers confirmed and validated the system-generated opportunities before escalating them for approval.

The results

The application of the solution led to workover and intervention candidate evaluations being routinely performed on a weekly basis (compared to the bi-yearly approval reviews previously held) across almost 200 well completions. In its early evaluation, the solution, has provided 89% time savings in both the identification and review of intervention candidates. Additionally, 88% cost savings have resulted from the elimination of manual work. 

 

Redefining seismic interpretation: 80% time-reduction

 

The challenge

Seismic interpretation has been a cornerstone of E&P activities for several decades, however the interpretation and extraction of value from such datasets still poses a major operational bottleneck in the overall geology and geophysics (G&G) workflow. Highly manual and subjective workflows, with a high degree of repetition combined with vast quantities of data, results in a time-consuming process which frequently yields inconsistent results.

The solution

The application of machine learning (ML) in fault identification, interpretation, and as a feeder to the reservoir modeling workflow, is optimizing reservoir development. The use of pretrained ML model fault prediction yielded a moderately accurate prediction of the total structures within the input seismic cube. This fault prediction provides a basis for initial fault extraction and structural modeling; whilst also highlighting areas where the prediction result could be improved.

The results

Extending the application to a user-driven ML model, with user-defined fault labels, yielded a significant increase in the accuracy of the fault prediction. This set of faults was validated by the geophysicist and used to inform a second structural model realization.

Using an augmented approach to fault identification significantly improved the domain-science driven workflows and delivered substantial value to the interpretation loop—an average 80% time-reduction in the overall structural interpretation was achieved for this project.

 

Smart Surveillance: Automatically interprets field data 24/7

DELFI Cognitive AI

 

The challenge

Digital oilfield projects integrate a large variety of data for production surveillance and technical management. The typical data includes well and flowline operational parameters, production rate estimations, reservoir models, and sporadic events such as well intervention and well tests. All of these are housed within industry standard historians, SCADA systems and proprietary databases. 

The solution

Access to high frequency records and sporadic data with their respective time and date , enabled the application of data analytics and machine learning solutions. During any incidence, the SCADA system provides the required data about operational conditions, such as well head pressures and temperatures, and well status and valve positions, this enables the usage of AI to measure the response from the well during a specific operational event.

Using an AI solution, we apply pattern recognition, machine learning, and data-driven analytics to common cases of petroleum production operations for naturally flowing wells. These cases include:

  • The automated identification of the reservoir environment (fractures, matrix, or dual porosity) based on bottom hole or wellhead pressure tendencies, which can identify radial or linear transient flow.

  • The diagnosis of water-cut instabilities based on changes of the pressure drop through the wellbore, altering the well head pressure declination rate.

  • The formation of hydrates based on surveillance of the flowline pressure variations

  • • The identification of uncalibrated choke valves in the surface equipment. 

The results

The solution automatically interprets field data (24/7) and provides valuable information to the technical team—it has become a valuable assistant for the petroleum engineers. The solution enhances the supervision and interpretation levels, which has led to increases in efficiency, improved HSE levels, and several economic benefits.