Periodic Reporting for period 2 - OptiDrill (Optimisation of Geothermal Drilling Operation with Machine Learning)
Okres sprawozdawczy: 2022-07-01 do 2024-12-31
Geothermal drilling industry faces various challenges such as poor overall drilling confidence and performance, lack of bottom hole awareness, lengthy tripping times, etc resulting in significant Non-productive Time (NPT) and unpredicted additional costs which makes the drilling process quite uncertain and expensive. Furthermore, there is a lack of digitisation and automation in the geothermal drilling industry and currently drillers mainly rely on personal skills and previous experiences.
The OptiDrill project aims to develop an innovative drilling advisory system utilising novel sensor systems and AI-based methods to predict and optimize the rate of penetration (ROP), drilled lithology, drilling problems, well completion, and enhancement and finally to unite those methods under one system to enable drilling process optimisation and intelligent decision making. Through the use of new technologies, the number of days taken to drill and complete wells will be greatly reduced, which in turn lowers costs and reduces risk and uncertainties. OPTIDRILL´s advisory system is based on a combination of enhanced monitoring systems, and multiple data-driven AI modules, each being responsible for either analysis, prediction, or optimisation of one aspect of the drilling, well completion, or well enhancement process.
The overall objectives of the project are
a) Digitalise the manual drilling data and text-based reports into one unified database
b) Instrumentalise the drilling process through the Implementation of the drill rig and BHA-compatible novel sensor strings
c) Implement novel system identification methods in the sensor string and monitoring systems
d) Employ the combination of machine learning and novel deep learning methods in drilling, well completion and enhancement modelling, performance prediction and optimisation
f) Predict and trigger detection of drilling problems through data-driven statistical and machine learning methods
g) Understand and predict the real-time lithology of the formation
e) Use Federated machine learning scheme in combination with self-learning machine learning algorithms to make the unified OptiDrill system
The OptiDrill project aims to develop an innovative drilling advisory system utilising novel sensor systems and AI-based methods to predict and optimize the rate of penetration (ROP), drilled lithology, drilling problems, well completion, and enhancement and finally to unite those methods under one system to enable drilling process optimisation and intelligent decision making. Through the use of new technologies, the number of days taken to drill and complete wells will be greatly reduced, which in turn lowers costs and reduces risk and uncertainties. OPTIDRILL´s advisory system is based on a combination of enhanced monitoring systems, and multiple data-driven AI modules, each being responsible for either analysis, prediction, or optimisation of one aspect of the drilling, well completion, or well enhancement process.
The overall objectives of the project are
a) Digitalise the manual drilling data and text-based reports into one unified database
b) Instrumentalise the drilling process through the Implementation of the drill rig and BHA-compatible novel sensor strings
c) Implement novel system identification methods in the sensor string and monitoring systems
d) Employ the combination of machine learning and novel deep learning methods in drilling, well completion and enhancement modelling, performance prediction and optimisation
f) Predict and trigger detection of drilling problems through data-driven statistical and machine learning methods
g) Understand and predict the real-time lithology of the formation
e) Use Federated machine learning scheme in combination with self-learning machine learning algorithms to make the unified OptiDrill system
The following activities were achieved in the reporting period:
1. End-user requirement analysis has been defined
2. Identification of key performance indicators (KPIs) to benchmark the success criteria of individual technologies being developed. This will be combined with defined Objectives and Key Results (OKR) to monitor progress of all stages.
3. Created drilling scenarios with possible drilling, completion and well stimulation problematic scenarios
4. Validation site selection process
5. Sensor and data acquisition system design specification is developed
6. The sensor types and configurations are defined and selected
7. Network patient analysis strategy is defined and initial analysis has been performed
8. Developed data lifecycle and curation strategy of the data collected
9. OptiDrill database has been designed, developed and populated
10. Description of the monitoring System Control Unit has been developed
11. Unified Data Acquisition Platform developed for the monitoring system
12. Developed specifications and performed tests for sensor housing connectivity, sensor housing multi-scale design and drill rod connectivity
13. Developed data management plan and project management plan
14. Developed and updated dissemination and communication strategy, plan and activities
15. Developed initial exploitation strategy planning
1. End-user requirement analysis has been defined
2. Identification of key performance indicators (KPIs) to benchmark the success criteria of individual technologies being developed. This will be combined with defined Objectives and Key Results (OKR) to monitor progress of all stages.
3. Created drilling scenarios with possible drilling, completion and well stimulation problematic scenarios
4. Validation site selection process
5. Sensor and data acquisition system design specification is developed
6. The sensor types and configurations are defined and selected
7. Network patient analysis strategy is defined and initial analysis has been performed
8. Developed data lifecycle and curation strategy of the data collected
9. OptiDrill database has been designed, developed and populated
10. Description of the monitoring System Control Unit has been developed
11. Unified Data Acquisition Platform developed for the monitoring system
12. Developed specifications and performed tests for sensor housing connectivity, sensor housing multi-scale design and drill rod connectivity
13. Developed data management plan and project management plan
14. Developed and updated dissemination and communication strategy, plan and activities
15. Developed initial exploitation strategy planning
The OptiDrill Advisory system will be a comprehensive and statistically assessed, drilling process cloud-based database optimization system, based on datasets provided by drilling operators and national data repositories, to cover multiple types of formations, geological settings, and drilling processes through knowledge transfer into the OptiDrill system. The provisioned database system will be statistically assessed and will provide a rating system for parameter importance in each step of modelling to avoid lower prediction efficiency through feed of unnecessary inputs into the system.
The Machine learning models of ROP, lithology, drilling problems, well completion & enhancement and coupled drilling optimization models to be developed through the OptiDrill project are designed to improve ROP, lifetime and reliability compared to existing technologies. OptiDrill will enhance the growth of geothermal energy as it will enable to exploit deep geothermal energy to generate energy by reducing the capital expenditure (CAPEX) and operational expenditures (OPEX). OptiDrill’s reduction of drilling operation time, will also lead to significant reduction of the environmental impact during drilling activities.
The expected impacts of the project are
• OptiDrill coupled drilling optimisation ML models in drilling advisory system will enable drillers to reach cost-effectively greater depths and higher temperatures on all types of geological formations for geothermal well drilling by
o Reduction of drilling time
o Reduction of unit cost of drilling
o Reduction of well completion cost
• Enhance the environmental performance of geothermal plants
The Machine learning models of ROP, lithology, drilling problems, well completion & enhancement and coupled drilling optimization models to be developed through the OptiDrill project are designed to improve ROP, lifetime and reliability compared to existing technologies. OptiDrill will enhance the growth of geothermal energy as it will enable to exploit deep geothermal energy to generate energy by reducing the capital expenditure (CAPEX) and operational expenditures (OPEX). OptiDrill’s reduction of drilling operation time, will also lead to significant reduction of the environmental impact during drilling activities.
The expected impacts of the project are
• OptiDrill coupled drilling optimisation ML models in drilling advisory system will enable drillers to reach cost-effectively greater depths and higher temperatures on all types of geological formations for geothermal well drilling by
o Reduction of drilling time
o Reduction of unit cost of drilling
o Reduction of well completion cost
• Enhance the environmental performance of geothermal plants