Artificial Intelligence (AI) is the ability of machines to perform tasks that conventionally required human cognition. AI is estimated to create economic value of $ 13 trillion by 2030 with an impact equivalent to $ 173 billion to oil and gas industry. In light of the huge disruption potential of AI, ONGC Management has integrated the digital use case of 'Predictive Exploration' as part of ENERGY STRATEGY 2040. AI is the core component of Predictive Exploration.
I write this article in an effort to propose 9 potential use cases of predictive exploration in well logging. The rest of this article provides a brief summary of AI, current AI opportunities and trends in E&P industry, upstream AI adoption themes. This is followed by a detailed description of predictive exploration use cases in well logging and current limitations of AI in geoscience.
Artificial intelligence is a sub field of data science , the science of extracting knowledge & insights from data. Machine learning (ML) , an AI paradigm is attributed to most of the recent progress in the field of AI. ML teaches machines to perform specific decision tasks without explicit programming. ML techniques can be broadly classified into supervised ML and unsupervised ML. Supervised ML can be succinctly summarised as — teaching a machine to learn a mapping from Input to output ( A to B ) by training it with several examples of (A,B). Once the training is complete , the machine is said to have learned a 'model'. This model could then be used to predict B, given A.
Unsupervised learning is useful for initial data exploration to find interesting structures in data.
ML predictions ( if reasonably accurate ), could be used for automation of tasks in existing human centric workflows. Hence, AI could be understood as a general purpose technology for automating tasks in a workflow. The deliverable of an AI implementation and Supervised ML in particular is a task specific model, that would automatically predict an output for a given input. It is necessary to emphasise that current AI solutions are very task specific. Models trained on one task are suited to performing the said task only.
In its current state, AI can automate any task / decision which takes a human about 1-2 minutes to make. But there is a negative test of appropriateness of applying AI to a particular task — Does the task consist of well defined rules and / or empirical relationships ? If yes, AI might not bring value to the task. AI is useful for tasks with a lot data ( examples of input - output relationship ) and complex relationships between input and output. The value that AI brings to these tasks is learning patterns and relationships present in existing examples and producing statistically consistent outputs given new inputs. Mathematically speaking, ML models are universal function approximators that learn complex functions from observations. This makes ML models better at learning from 'unknown knowns' and 'known knowns' present in data and predicting outputs better and faster than humans.
From an economic standpoint, AI makes prediction ( a fundamental component of intelligence ) cheaper. Since prediction complements judgement which drives business decisions, AI has been transforming businesses by automating repetitive tasks , accelerating workflows and making the businesses more data driven.
The ongoing evolution of the E&P industry, dubbed as Oil & Gas 4.0 has one core goal — to achieve greater business value through adoption of advanced digital technologies. Among these, AI & ML have shown huge potential in improving efficiency by accelerating processes and reducing risk.
In particular, the upstream industry is capital intensive and characterised by high risk and uncertainty. Further to exacerbate the challenges, upstream processes rely on expert knowledge , involve subjective perception and experience based decision making, are time and resource intensive , sometimes have no objective measurement criteria.
Hence, there is huge potential for ML / AI tools to not only accelerate and de-risk processes but also establish quality baselines and bring consistency to decision making inputs in upstream.
E&P industry is already seeing a few broad themes in exploiting AI tools, namely reducing the 'Time to Value' of the gathered data and building a 'Living Earth Model' which automatically updates with new knowledge. Further, most use cases have been targeted towards
- Data driven studies as a faster and less accurate alternative physics driven studies.
- Providing baseline interpretation metrics for subjective results.
- Automating data quality checks
In line with the industry needs, the most successful use cases of ML / AI in upstream have been the ones which provide tangible process acceleration benefits and significant reduction of human errors in mapping hydrocarbon targets.
- Tools for automated mapping of reservoir rock properties over an oil region ( accelerated from several weeks to several seconds )
- Tools for extracting geological information from well logs ( 100 + times speed up )
- Tools for rock typing based on images of rock samples extracted from wells ( ~1,000,000 times speed up )
As a compliment to industry adoption, there has been increased activity in the scientific community in applying ML to geoscience. Recent times have also seen a significant AI drive by professional bodies like SPE, EAGE, SPWLA, SEG. The number of quality research publications in scientific journals and conference proceedings has seen a steady rise. The industry's estimate of AI's current value can be judged by high reward ( $ 100,000 ) open competitions like 'Salt Segmentation in Seismic Cubes'. This is further augmented by community contributions to developing ML applications with business impact. For instance, the code implementations of most competitions have been open sourced to tinker and build on.
AI in Well Logging
Well logging gives precise information about various physical properties of the sub surface along the well bore, with measurement resolution in centimeters. Well logging employs sensors which measure electrical resistivity, natural gamma ray intensity, response to magnetic excitation, neutron density and some others. Petrophysicists use well logging data for their interpretation routine, including rock typing , estimation of porosity and permeability and estimation of relative fluid saturation along the well bore. Petrophysical interpretation is a time consuming process and its results strongly depend on the domain expert. ML can reduce time and bring consistency to interpretation results.
In view of this, I propose the following AI use cases of Predictive Exploration in well logging
- Automatic Reconstruction / generation of missing sonic logs
- Automatic Lithology prediction
- Generating Density logs from other logs
- Automatic NMR generation
- Predicting porosity, permeability and water saturation using well logs
- Auto Interpretation of CBL Logs
- Auto log QC
- Log Recommender System
- Auto Sonic Correction using VSP & Facies
( Note that research papers with various levels of evidence are hyperlinked for use cases # 1-6 . Use cases #7-9 seem very feasible in view of their similarity with the other use cases. )
Automatic generation of missing sonic logs is very useful to generate sonic logs for old wells to feed into well-seismic tie workflows. Automatic lithology prediction can be explored as a fast alternative to detailed lithology analysis carried out in core laboratories. Generating missing density logs using existing logs can be used to provide missing data for feeding into reservoir characterisation workflows. Automatic NMR generation namely MPHI (effective porosity), MBVI (irreducible water saturation), and MPERM (permeability) from conventional well logs can be used to improve the estimation of economically recoverable hydrocarbons. Estimation of porosity, permeability and water saturation from well logs is a faster alternative baseline to the empirical calculations. Auto interpretation of CBL logs not only accelerate cement integrity checks but also brings consistency to expert driven analysis. Auto Log QC can be employed provide an automatic mechanism to check log quality before ingestion into the G&G data warehouse.
Log Recommender system is a recommendation engine aimed to reduce the volume of physical log measurements. The current state of the art hypothesises that most logs could be reconstructed or generated from existing well logs. Hence, a recommender system can be built to provide recommendations on the logs to be recorded based on the log reconstruction accuracy / quality.
Auto Sonic Correction using VSP & Facies aims to provide environmental corrections to sonic logs recorded in wells where VSP was not undertaken, by learning the same from existing VSP survey data.
For the implementation of # 1-4, an ML model could be trained specific to each use case. The ML model is trained on logs recorded in existing wells in the field to predict logs / properties in different well. For example, let us consider the use case of Generation of missing sonic log. Say, Wells A & B have gamma ray, density and sonic logs and Well C has gamma ray and density, but sonic log is missing. One may create a dataset of Well A & B with inputs as gamma ray log and density log , output as sonic log. The trained ML model can be used to predict sonic log of Well C using the gamma ray log and density log recorded in Well C.
ML models learn from underlying statistical relationships between existing inputs and outputs to predict outputs for new inputs. In the case of well logging, in the fields that exhibit adequate homogeneity in the characteristics of sub surface through out its extent, the existing field data could be used to predict field properties.
For the use cases # 5,6 , ML models can be trained on datasets created from existing well log data where the needed output is already ascertained by an expert. For example, for auto interpretation of CBL logs, experts could divide the log data into equal segments and assign a cement quality index of 1-6 for each segment. ML model could be trained to predict cement quality index for an input of log curves. The trained model could be used to automatically predict cement quality on new CBL log data in the same field. These predicted results could be used as rapidly generated baseline results for further interpretation. Auto log QC can be developed in a similar manner.
Although more advanced use of AI is possible, the above use cases represent the 'low hanging' fruit for the following reasons.
- The first challenge for ML is creating quality datasets. Datasets with well defined structure are an easy target for building models that scale well. Well logs are almost structured data. ( Data structure is constant for each service / technology provider )
- Our G&G data warehouse already stores well logs in a well structured format. This could be leveraged to create field specific models. Further, some predictions can be automatically performed in the background for each log upload and generated as files for immediate consumption.
Please note that this article only covers some possible aspects of the geoscience parts of well logging. Operations and business process related use cases are not covered.
References :
- Prediction Machines
- Artificial Intelligence in Oil & Gas Upstream : Trends , Challenges and scenarios for the future
I hope this helps each stakeholder in the conversation around the current digitalisation initiative.
I am excited about this new venture of ONGC and would be delighted to explore these use cases further.