From theory to practice: Automating the development of infill drilling business cases for brown-fields

Intruduction. The article discusses an approach to automating the development of infill drilling business cases for brown fields.

Aim. The aim of the work is to develop and test a module for the automated search for promising zones and placement of the project wells (“AVNS”) for generating infill drilling business cases at brown fields, which allows to reduce the influence of subjective factors and labor costs.

Materials and methods. The developed module includes the following stages:

• pre-processing of geological and production data;

• constructing an Opportunity Index map; 

 • clustering of promising target zones and placement of project wells;

• calculating well start-up parameters;

• assessing economic efficiency.

The algorithms are implemented using machine learning methods, statistical analysis, and common analytical approaches. Testing was conducted on data from more than 40 productive formations.

Results. A retrospective analysis showed high accuracy of the recommendations, comparable to expert decisions, with moderate coverage. Furthermore, untapped prospective zones were identified, indicating additional drilling potential. A key practical result was a reduction in labor costs for business case preparation by 20%.

Conclusions. The study demonstrates that an automated approach can enhance the efficiency of drilling planning for brown assets; however, it requires further development in terms of improving input data quality, refining algorithms, and integrating with other planning systems. 

1. Prokhorov A.A. et al. Modern Approaches to Automating the Infi ll Drilling Process on Mature Fields: From Selection of Infi ll Wells Locations to Cost Optimization. Gazprom Nefl STC. 2023, pp. 159–172. (In Russ.)

2. Bishop C.M. Pattern Recognition and Machine Learning. Springer. — 2006. — pp. 41-45.

3. Litvak M., Gane B., Williams G. Field Development Optimization Technology. Society of Petroleum Engineers, Houston. 2007. SPE 106426, pp. 1–10.

4. Roussennac B., Gijs van Essen Streamlining the Well Location Optimization Process — An Automated Approach Applied to a Large Onshore Carbonate Field. Society of Petroleum Engineers, Dubai, UAE. 2021. SPE 205913, pp. 1–13.

5. Boah E.A., Senyo Kondo O.K., Borsah A.A. Critical Evaluation of Infi ll Well Placement and Optimization of Well Spacing Using the Particle Swarm Algorithm. Journal of Petroleum Exploration and Production Technology. 2019, vol. 9, no. 4. https://doi.org/10.1007/s13202-019-0710-1