Optimization of infill drilling locations

Transcription

1 Digital Technologies INVESTMENT OPPORTUNITY: Optimization of infill drilling locations

2 Executive summary Today, decisions on drilling locations are made by using reservoir models and simulations, but with limited use of analytics and models based on artificial intelligence (AI). Machine learning/ai algorithms can also be used to identify the most prominent infill drilling locations and key factors for uncertainty in reservoir production. The objective is to increase hit rates from new wells, increase production rates from existing wells, and achieve better forecast accuracy for new wells. Operators in Qatar have started digitalization of oil fields (i.e., intelligent oil field) but have not yet implemented advanced analytics. Doing so will generate an estimated total demand for services of QAR 110M over five years (including implementation and ongoing support spend). Since there is a growing market for subsurface analytics services in Qatar, international suppliers with a local presence could deliver the services needed to implement and sustain drilling location optimization by using advanced analytics. 2

3 Detailed opportunity description Optimization of infill drilling locations Text Optimization of infill drilling is the use of machine learning algorithms to identify the most prominent infill drilling locations and key factors for uncertainty based on all available field production and drilling history data including: Production history for individual wells Well logs /well tests Drilling reports from other nearby wells. Optimization of infill drilling will be built on top of ongoing projects to digitize and integrate subsurface data. Pilots will be crucial for success and prove value capture. The following applications may not be included in scope: Reservoir injection optimization Optimization of pressure setting at well heads. Localization opportunity Business scope Identification of the optimal drilling location based on all available data using machine learning services include: Data structuring, cleaning, and preparation Data analytics and modeling Change management for drilling and reservoirs groups Out of scope Software and licenses Access to analytics platform for development and testing of new models Front-end visualization and application tools for maintenance personnel Hardware requirements: IT infrastructure/iaas Capital intensity Low (QAR 0-10M) Medium (QAR 10-50M) High (>QAR 50M) 3

4 Market size Estimated spend (Qatar only) on optimization of infill drilling locations, QAR M Implementation spend Ongoing spend Fields/assets with optimized drilling rolled out, number Key highlights Currently, operators are working to digitize and integrate subsurface data however, the use of AI and machine learning is nascent. Within the next five years, we expect optimized infill drilling to be implemented across approximately 20 relevant offshore fields in Qatar, resulting in a total demand of QAR 110M. For each field, there is an expected total consulting spend of QAR 2M (depending heavily on access to data). In addition, a total spend of QAR M for licenses and hardware is projected per field. There is opportunity for best-in-class international suppliers to set up a base in Qatar or establish a JV together with local suppliers. Relevant stakeholders Potential buyers* All Qatari upstream oil and gas players *Examples shown not exhaustive 4

5 Disclaimer The material, data, charts and pieces of information contained in this document ( Information ) is for general information purposes only and should not be construed as an investment or commercial advice or as a recommendation whatsoever. The user of the Information ( User ) is responsible for independently verifying the Information and shall make his own determination as to how suitable the Information is for his own usage and intent. User should not rely upon the Information as a basis for making any business or investment decisions. Whilst Qatar Petroleum endeavours to provide and keep the Information up to date and correct, Qatar Petroleum makes no representations or warranty of any kind, express or implied about the content, completeness, accuracy, quality, reliability or suitability with respect to the Information for any purpose. Qatar Petroleum expressly disclaims liability for errors and/or omissions in the Information contained in this document and shall in no event be liable for damages resulting from the use or reliance of User upon the Information. Any reliance the User of the Information places on such Information is strictly at User s own risk. 5

6 Website: