Digitalization of Formation Damage Candidate Screening Workflow Improves Process Efficiency

Gupta, Anish (PETRONAS) | Narayanan, Puveneshwari (PETRONAS) | Trjangganung, Kukuh (PETRONAS) | Mohd Jeffry, Suzanna Juyanty (PETRONAS) | Tan, Boon Choon (PETRONAS) | Awang, M Rais Saufuan (PETRONAS) | Badawy, Khaled (PETRONAS) | Yip, Pui Mun (PETRONAS)



A matrix stimulation candidate screening workflow was developed with the objective to reduce the time and effort in identifying under-performing wells. The workflow was initially tested manually for few fields followed by inclusion in Integrated Operation for an automated screening of wells with suspected formation damage. Analysis done in three fields for stimulation candidate selection will be displayed with actual statistics.

The main aim of the work was to digitalize the selection of non-performing candidates rather than manually looking into performance of each well. A concept of Formation Damage Indicator (FDI) was combined with Heterogeneity Index (HI) of the formations to screen out the candidates. Separate database sets of Reservoir engineering, Petrophysicist and Production was integrated with suitable programming algorithms to come up with first set of screened wells evaluating well production performances, FDI and HI trends up to over the last 30 years. The shortlisted candidates were further screened on the basis of practical approach such as gas lift optimization, production trending, OWC-GOC contacts, well integrity and well history to come up with second round of screened candidates. The final candidates were analyzed further using nodal analysis models for skin evaluation and expected gain to come up with type of formation damage and expected remedial solution.

For fields A and D with a total of 210 strings each, the initial FDI and HI screening resulted in 70 and 120 strings being shortlisted, respectively. This was followed by a second round of screening with 25 and 35 strings being further shortlisted as stimulation candidates, respectively. Nodal analysis models indicated presence of high skin in 90% of the selected wells indicating a very good efficiency and function-test of the workflow. In addition to selection of the candidates, the identification of formation damage type was compiled on an asset-wise basis rather than field basis which helped in more efficient planning of remedial treatments using a multiple well campaign approach to optimize huge amount of cost. The entire screening process was done in one month which was earlier a herculean task of almost one year and much more man-hours. With effective manual testing of the workflow in two major fields, workflow was included in Integrated Operations for future automation to conduct the same task in minutes rather than months.

With this digitalized unique workflow, the selection of under-performing wells due to formation damage is now a one click exercise and a dynamic data. This workflow can be easily operated by any engineer to increase their operational efficiency for flow assurance issues saving tons of cost and time.