Technological trends and scaling-up to deliver our vision
Commentary by Tatiana Moguchaya, CEO, Earth Science Analytics.
21 September 2021
Reflecting on my first three months as CEO
After three months as CEO, we come to the end of 2021; a natural time to reflect but more importantly, to look forward. Over the last 12 months, the organization has made several strides on its journey to change how industry and businesses operate, to streamline the conventional E&P workflows, as well as to accelerate Energy Transition in order to deliver on the ESG and sustainability goals.
To fully understand our place in the market, I think it’s important to outline what first attracted me to the position of CEO. Firstly, I wanted to embrace the challenge that this leadership role presented and the opportunity to test my skills and expertise across all operational and strategic levels. Secondly, the vision and ground-breaking technology of ESA were ones I found particularly exciting. Creating business value from industrial data has always been a key part of our operating model. It’s also an area we are looking to enhance over the next 12 months. Lastly, and most importantly, I am extremely proud and humbled by leading such a brilliant team.
Looking to the future
As an innovative software company, we provide proven technology and solutions that enable and facilitate the ongoing digitalization of the energy industry. We have built, and continue to build, the next generation of subsurface technologies and workflows to support those operating within E&P. Through ESA´s technology we can enable the drastic automation of workflows, increase quality of results, speed up workflow efficiency by 10x, and remove data and organizational silos and barriers. However, we see real scope to expand our market portfolio in 2022 and beyond, enhancing the capabilities of our solutions and increasing our activity. Technology is no longer something that’s just nice to have; it’s an operational requirement and the onus is on companies like ourselves to meet the digitalization needs of various sectors. There is particularly a sense of critical urgency when it comes to sustainability goals. For us to get there, we need tools like ML, and we need trust collaborations and partnerships. Done properly, it will come with a tremendous impact on the world itself. It’s a challenge we are set to fully embrace.
Our integrated data-centric MLOps platform, EarthBANK, and our EarthNET SaaS business applications will support this. The multi-cloud MLOps platform and toolkit provide the capability to rapidly train pre-built models and facilitate innovative state-of-the-art machine learning (ML) solutions. Its usage directly enhances subsurface, drilling and production workflows and it is widely known and respected within the oil and gas industry. An increased use of data by companies may support the belief that industry has fully embraced the concept of digitalization. However, while increased data is undoubtedly being gathered, I’ve always held the belief that data alone is a commodity and it’s not enough to simply collect data and visualize it in a dashboard. Data needs to be cleaned, normalized, and contextualized to deliver real insight and support fully accessible ML. EarthBANK facilitates this and supports generation of synthetic data in order to increase the fidelity of ML models. The globally accessible, customizable SaaS applications for ML workflows help furthermore to transform industry data into real business value.
Enhancing our solutions
An area of real focus this year has been the expansion of the types of data that we can process using the power of cloud computing and storage. Integrating both structured and unstructured data from different sources and related to different types of measurements, e.g. well data, seismic data, core photos, cuttings images, fluid samples, etc. in the G&G domain, creates higher ontology and provides holistic predictions. As part of this, we recently announced a collaboration with Rockwash Geodata. We have combined their technical expertise with our industry-leading software, transforming vast quantities of what were dormant oil and gas data types into high-value digital assets.
In the first project under this partnership, Rockwash Geodata experts have taken a proprietary database of cuttings photographs and, using our ML workflows, categorized the photos in terms of bulk lithology. This has created a fully labelled dataset of cuttings sample images.
The combination of these newly created digital inputs with a quality-assured set of traditional log suite curves prepared by ESA, has led to the generation of high-quality rock property predictions. These can now be used to build larger, reservoir-scale interpretations. Ideally, we ought to treat the data so that each natural grain of the subsurface has a digital identity and can contribute to better intelligence for characterizing the rocks and fluids.
By enhancing the data we analyse, we have provided another layer of understanding for those working with petrophysical and geoscience workflows. This will directly support the ability to make confident business decisions when reviewing rock property predictions. We look forward to building on what we have already achieved over the coming months, helping to untap business value in what would otherwise be dormant data.
Opportunities for growth
ESA´s mission is to democratize access to AI to help our customers and partners to become more competitive in their processes, products, and services and to generate real business value, fast, accurately, and repeatedly.
We will continue with our core business of improving, accelerating and streamlining E&P activity across subsurface, drilling and production workflows. There is no doubt that energy companies and the G&G industry have to move more rapidly from the POC stage to transforming the workflows as a
whole. ESA is well placed to support and facilitate this transition.
However, next year will also see us scale up our domain-specific AI solutions across alternative industries. Due to our vast technical offering across MLOps, DevOps, Data Labelling and DataOps, we are well placed to diversify into markets like renewables and asset management. We have ambitions to scale our proven value to other verticals by using our deep domain expertise in geoscience, data science, and state of art technology in areas adjacent to our traditional oil and gas activity. This vision will support those operating in multiple sectors to unlock the full potential of data and realize the business and operational value of AI.
To my mind, this is the true meaning of digitalization, and the journey to achieve it is one we are proud to be leading from the front.
2022: what to expect?
Will 2022 be another year of living dangerously or smoother sailing for those businesses that tackle the uncertainty with a firmer embrace of AI? If we can take one lesson from the last twelve months, it’s that operational agility is crucial. An increase in home working has been one of the real catalysts behind a renewed digital-first approach. Several trends are seen as a commonality and will continue to evolve in 2022.
Increased Trust in AI: Backed up by the increasing amount of proven success stories and concrete value realization.
Hyper-Accelerated AI Adoption: There’s no doubt the continuing pandemic has created an era of accelerated invention and reinvention for many businesses and scientific organizations. The goal is to create short-term measures that meet the needs of the day while building for long-term gains and radical change. As part of this, it would be fair to predict increased demands for solutions within DataOps and MLOps. The former must ensure that data is ML-ready by cleaning and contextualizing. The latter must provide the capability to rapidly train pre-built models and bring innovative ML solutions to those who need them. We have seen first-hand the increased adoption – and expenditure – on AI initiatives this year, and we expect more industries will seek to adopt and integrate AI applications within their operations in 2022.
AI-on-5G will unlock new edge AI use cases. These include “Industry 4.0” use cases such as site automation, remote monitoring and inspection, predictive maintenance and performance optimization. The promise of 5G will open new opportunities for edge computing. Key benefits will include network slicing to allow assigning dedicated bandwidth to specific applications, ultra-low latency in non-wired environments, as well as improved security and isolation.
Accelerated Data Science Platforms: Data platforms will further modernize through end-to-end data pipelines, centralized infrastructure, Kubernetes-based applications, and best-in-class, fit-to-task storage.
Convergence of AI and OT Solutions: Further integration of AI and traditional OT management solutions will simplify the adoption of edge AI in industrial environments.
AI for ESG: Consumers and government entities increasingly will hold enterprises accountable for environmental impacts, social and corporate governance (ESG). Companies will invest in significant computational power to run AI models, including deep learning and natural language processing models, that analyze all the data necessary to track company performance relative to ESG.
Specialization over Full-stack MLOps: With maturing ML teams at organizations, data scientists and ML engineers are being enabled to work on their core jobs versus having to do it all.
Large Language Models to Advance Understanding: In 2022, we’ll see accelerated growth in adapting large language models (LLMs) to serve more industries and use cases. Trained on massive amounts of industry-specific data, LLMs will be able to answer deep domain questions.
MLOps Solutions with Emphasis on Collaboration: Organizations have been shifting work styles to support remote workers, hastened by COVID-19. A new set of governance capabilities focused on ML needs will arise.
The importance of our team
I am proud to work with such an innovative, diverse, highly competent and agile team. We all bring different nationalities, genders, skills and experiences to our respective positions, but more importantly, we bring motivation. We are all driven to deliver on the promise of digital transformation. We all share the belief that a transformed industrial world is possible. One that is powered by data and AI to make it more efficient, more secure and more sustainable, without sacrificing profits. Our team will continue to drive the efforts to achieve this over the coming years.
ESA is growing and expanding but we are only just at the tip of the iceberg. As outlined above, I see tremendous opportunities for growth; supported by a clear strategy, vision and a world-leading product.
As we come towards the end of the year, I want to express my thanks and gratitude to our investors, customers and partners for the trust shown, the support given, and for the opportunities to work together to scale up domain-specific AI and make an impact. Together we are delivering real digitalization across multiple sectors. I look forward to continuing this exciting journey as we enter 2022.