Application of the Combined Real-Time Petrophysical and Geosteering Model to Increase Drilling Efficiency (Russian)
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Drilling, as a direct and effective method of opening oil and gas layers, has been widely used. A reasonable combination of drilling tools plays a key role in increasing the rate of mechanical drilling, reducing drilling costs, and reducing downhole accidents. Conventional drilling relies on years of experience of on-site workers and reference to the operation of drilling wells, making use of drilling tools and lacking scientific basis. However, the reservoir situation is erratic, the unknown factors are very numerous, unpredictable, and the difficulty of drilling is increased. Drilling into unknown reservoirs, especially high-temperature and high-temperature risk wells, poses a huge threat to the lives of workers on site. Conventional drilling of known reservoirs will also encounter unknown problems such as drilling distance growth, stuck drilling, drilling tools falling, increased inclination, and deviation from the intended target position, which seriously reduces drilling efficiency, increases operating time, risk and drilling difficulty affected by the reasonable use of the drilling tool combination. With the development and application of computational intelligence, through the accumulation of massive geological property data, reservoir structure data, drilling tool parameters, construction data, drilling fluid parameters and other drilling data, intelligent drilling is used to predict unknown drilling information which can reduce the risk of drilling and improve drilling efficiency. In this paper, the work mode of "data running first, operation post" is used to further strengthen the application of drilling tools combination to improve the rate of mechanical drilling and reduce downhole problems.
Drilling, as a direct and effective method of opening oil and gas layers, has been widely used. A reasonable combination of drilling tools plays a key role in increasing the rate of mechanical drilling, reducing drilling costs, and reducing downhole accidents. Conventional drilling relies on years of experience of on-site workers and reference to the operation of drilling wells, making use of drilling tools and lacking scientific basis. However, the reservoir situation is erratic, the unknown factors are very numerous, unpredictable, and the difficulty of drilling is increased. Drilling into unknown reservoirs, especially high-temperature and high-temperature risk wells, poses a huge threat to the lives of workers on site. Conventional drilling of known reservoirs will also encounter unknown problems such as drilling distance growth, stuck drilling, drilling tools falling, increased inclination, and deviation from the intended target position, which seriously reduces drilling efficiency, increases operating time, risk and drilling difficulty affected by the reasonable use of the drilling tool combination. With the development and application of computational intelligence, through the accumulation of massive geological property data, reservoir structure data, drilling tool parameters, construction data, drilling fluid parameters and other drilling data, intelligent drilling is used to predict unknown drilling information which can reduce the risk of drilling and improve drilling efficiency. In this paper, the work mode of "data running first, operation post" is used to further strengthen the application of drilling tools combination to improve the rate of mechanical drilling and reduce downhole problems.
Conventional geo-steering approach use raw logging measurements to define wellbore positioning within the reservoir while drilling. The geo-steering specialist usually compares real-time logs to modelled logs (GR/Density/Neutron/Resistivity) and the geological model is then adjusted to make real-time decisions to deliver the well objectives. This conventional method is applicable to most reservoir conditions. However, it may be insufficient or inappropriate in heterogeneous reservoirs or wells with complex geological settings, potentially resulting in wells being sub-optimally placed and reducing the value of reservoir sections in terms of productivity. This paper aims to showcase a Petrophysics-based Geo-steering approach to maximize the value of reservoir sections. Geo-steering aims to place the well trajectory in the lithology with optimum storage capacity, flow capacity and hydrocarbon saturation. The method of log-to-log comparison is popular for its simplicity and speed of use in real-time but is not enough for certain scenarios. For example, the real-time log response can be very different from modelled log response in the presence of gas or very light oil, irrespective of petrophysical properties (porosity/permeability) being similar. Moreover, real-time Sw estimation would be required in addition to porosity to minimize the risk of drilling a producer into water bearing intervals. In fact, the comparison between petrophysical parameters is more appropriate to heterogeneous reservoirs or wells with complicated geology. This approach requires good co-ordination between geologist, petrophysicist and geo-steering specialist. Prior to drilling, the petrophysical model from offset wells should be defined and used to derive porosity, permeability and saturation. While drilling, the petrophysical properties are then interpreted in real-time and based on the comparison between modelled and real-time petrophysical properties, decisions are to be made with respect to the well objectives. An example with strong gas effect in a carbonate reservoir from Abu Dhabi is presented to demonstrate this novel approach. Real-time density/neutron does not have good correlation with modelled density /neutron due to gas effect. Such poor correlation can be attributed to proximity to a Gas Oil Contact (GOC) and dynamic invasion, complicating the real-time geo-steering. However, real-time total porosity from log analysis correlates very well with modelled total porosity, providing confidence in wellbore positioning and allowing the geologist and the geo-steering specialist to make the correct real-time decision to place the well in the optimum stratigraphic position in order to meet the well objectives. Only conventional logs are utilized in this case, but if real-time NMR and resistivity image interpretation are available, it will provide additional information in term of permeability, secondary porosity and irreducible water saturation to aid efficient geo-steering.
Real-Time AI Geosteering for Horizontal Well Trajectory Optimization
Active petrophysical geosteering in reservoir formations has become a very promising practice and commonplace to improve express formation evaluation and maximize hydrocarbon production from reservoirs. The present study showcases the effectiveness of utilizing artificial intelligence (AI) framework to process resistivity logging while drilling (LWD) data for the purpose of real-time optimizing well trajectories and maximizing hydrocarbon production in pay zones during drilling horizontal section. We introduce a novel framework that automatically adjusts planned well trajectories during horizontal drilling. The framework takes the planned wellbore trajectory, reservoir model porosity, and ultra-deep resistivity LWD data as input. Hydrocarbon saturation volume is then calculated using the Archie equation. Subsequently, optimization algorithms correct the planned trajectory to maximize wellbore production using hydrocarbon saturation volume. The framework delivers an optimal wellbore trajectory performing real-time formation evaluation, guiding the drill bit through highly saturated pay zones. The proposed framework was tested on a 2D synthetic dataset using various optimization algorithms, including reinforcement learning algorithms for continuous action spaces (PPO, DDPG, TwinDDPG) and Q-learning and evolutionary algorithms. The evolutionary group of optimization algorithms achieved the highest efficiency with baseline hyperparameter settings, improving cumulative oil saturation per drilled meter by up to 32.5%. Reinforcement learning algorithms needs to be further explored because they have promising results but still high computational complexity. The evolutionary algorithm was then verified on a 3D Groningen field dataset, determining optimal hyperparameters such as differential evolution strategy, population size, mutational constant, and maximum number of iterations. The framework improved cumulative oil saturation per drilled meter by up to 6%, significantly increasing the average number of penetrated hydrocarbon-saturated pay zones along the drilling path. The developed AI-based framework presents an innovative approach for real-time automatic correction of well drilling trajectories, maximizing well productivity. This method can significantly aid in the interpretation and optimization of decision-making related to geosteering.
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