Artificial Intelligence in Reservoir Engineering
1. How exactly a reservoir engineer would respond to an AI system with reference to a real oil/gas field scenario as on date?
2. Why should a reservoir engineer have any ‘Algorithm Aversion’ (the so called negative attitude towards using Algorithms), if AI could understand the reservoir heterogeneities precisely?
3. Whether the task of reservoir planning and management could be considered as ‘subjective’?
OR
Would it require attention to individual uniqueness (such as an expert in reservoir draining principles; or, an expert in reservoir characterization; or, an expert in reservoir risk and uncertainty quantification)?
4. How exactly an individual reservoir engineer’s perceptions of a ‘Reservoir Characterization and Drainage Principles’ (RCDP) could influence her/his attitudes and behavioral intentions, which, in turn affect their actual use of AI technology?
Whether a reservoir engineer would perceive the actual usefulness of AI technology towards RCDP;
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Would she/he perceive the ease of using AI technology?
OR
The way AI technology is presented, it gets framed, it gets designed, and the way it gets marketed – is going to influence the perceptions of a reservoir engineer towards AI’s usefulness and its associated ease of use?
5. Even while applying simple reservoir engineering principles, for example, how exactly a reservoir engineer would be able to deduce the average value of a reservoir permeability - depends on - striking a perfect balance between her/his sound theoretical knowledge as well as to the extent of data availability.
On top of it, if we focus the problem, purely based only on data (as expected by AI), even then, the way porosity data needs to be handled; and, the way, the permeability data needs to be handled have fundamental differences - arising from the fact that ‘porosity data pertain to Gaussian distribution’; while ‘permeability data pertain to log-normal distribution’.
On the other hand, mere data on porosity and permeability may not always help us to deduce the actual least resistive pathways, which the reservoir, by default would prefer (where, the dynamic capillary forces are overcome with ease over various pore-throat sizes)?
If so, to what extent, AI would be able to do justice towards incorporating and replicating the actual reservoir physics?
6. Leaving aside the engineering principles, to what extent, emotional and psychological responses associated with an individual reservoir engineer towards RCDP could play a crucial role towards reservoir planning and management?
Feasible for an AI to have a coupled cognitive and emotional responses to RCDP?
Suresh Kumar Govindarajan Although artificial intelligence (AI) has the potential to make a significant contribution to the comprehension and reproduction of reservoir physics, there are some restrictions that need be taken into account.
In reservoir physics, fluids, rocks, and their interactions in subterranean reservoirs are all subject to complex behavior. AI can help with data analysis, pattern recognition, prediction, and optimization, among other reservoir physics-related tasks. Large-scale data processing, pattern recognition, and prediction capabilities are all capabilities of AI systems.
In reservoir characterization, where it can examine seismic data, well logs, and other geological information to produce precise models of underground reservoirs, AI can be especially helpful. AI can assist in identifying important reservoir parameters like porosity, permeability, and fluid by applying machine learning approaches.
It is crucial to remember that AI models are founded on statistical correlations and patterns acquired from past data. models may not completely represent the underlying physical processes, even while models can capture and mimic some features of reservoir physics. It can be difficult to properly mimic reservoir physics using AI models alone since it incorporates a variety of very complicated and non-linear phenomena, including fluid movement, multiphase interactions, and geomechanically impacts.
As a result, even while AI can aid in reservoir characterization and management and provide useful insights, it should only be used in conjunction with conventional physics-based models. The accuracy of predictions can be increased and reservoir behavior can be better understood by combining physics-based models with AI techniques.
While AI can assist in certain aspects of reservoir engineering by analyzing data and predicting behaviors, fully replicating the intricacies of reservoir physics remains challenging due to the complexity of processes, data limitations, and the need for domain expertise. AI often serves as a complementary tool rather than a complete replacement for traditional reservoir simulation and engineering practices.
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