Using Machine Learning-Based Data Factory to Unlock Mining in Australia for Environmental, Social and Corporate Governance (ESG)

EAGE Annual, Vienna, Austria | June 2023

The road to net zero requires a lot of raw materials from the mining industry. Renewable energy systems for solar, hydro, and wind need to be built to support the transition. Among the many metals critical to technology and infrastructure necessary for new energy, copper is highly sought after thanks to its conductive efficiency making it an irreplaceable element of any electrical equipment. Therefore, it is projected that by 2050, the demand for copper will reach more than 53 million metric tons.

Sand Production and Control Benchmarking Through Unstructured Data Analysis with Machine Learning in the North Sea

EAGE Annual, Vienna, Austria | June 2023

Sand production has been serving as a bottleneck to the oil and gas industry, contributing to disruption of daily production operations, casing deformation, erosion of well tubing, pipelines, and surface equipment, expediting significant non-productive time (NPT) costing millions of dollars in loss annually. The study first creates a relationship between the causation of sand production versus the sand control practices implied and best practices are derived from the practices of multi-wells.

Double Funnel Approach for Screening of Potential CO2 Storage Opportunities in The Norwegian Continental Shelf

EAGE Annual, Vienna, Austria | June 2023

In this study, the integration of Machine Learning (ML) whereby the reports from the Norwegian Petroleum Directorate (NPD) are ingested into one platform creates a potentially cost-effective solution by screening previous knowledge gathered for depleting oil and gas fields and significantly reduces the time of the screening, the evaluation and the ranking of CCS prospects.

CO2 Emissions The Elephant in The Room – A Pathway of Reduction Using Digitalization and Unstructured Data  

EAGE Annual, Vienna, Austria | June 2023

In this paper, we are exploring the challenges associated to climate change in the energy industry with the paradigm of extracting oil and gas in a low CO2 environment to limit the effect of climate change and provide the world with an affordable source of energy for mobility and heat generation. We will be discussing how carbon accounting allows to track direct and indirect source of emissions, its origins and the challenges associated to them.

Utilizing Machine Learning to Gain Geological Insights through Unstructured Data for Sustainable Activities – Case Study Pre-salt Campos and Santos Basin, Brazil  

EAGE Digital Innovation, Bangkok, Thailand | 2022

Understanding the basin regional trends and identifying the anomalies is a crucial background research during basin exploration activities. One way to gain a sound knowledge about the geology and the exploration history is to analyse the vast amount of data accumulated over the years in an unstructured manner. A sustainable data driven strategy leveraging on the latest advancement of Machine Learning (ML) and Analytics is applied on vast amount of unstructured data.

SUSTAINABLE Data Mining Combination of Machine Learning Based – Parameter Extraction, Data Visualization and Data Connectivity to Efficiently Upcycle Big Data for Basin Analysis 

APGCE Kuala Lumpur, Malaysia | 2022

In the upstream oil and gas sector, the processes of data mining, which involves searching, extracting, and validating information that sits within the technical documents, reports, presentations, and studies to understand exploration history and geological parameters are often challenging and requires vast resources to be completed. Yet, many geological information that have already been mined, are stored in spreadsheets or niche databases, that limits their abilities to be recycled for multiple uses within the organization .

Scaling and Optimizing Performance and Cost of Machine Learning Ingestion on Unstructured Data for Subsurface Applications

EAGE Annual, Madrid | 2022

In recent years, the energy industry has shifted their attention into extracting additional values from their in-house legacy datasets for shorter project turnaround and better decision making. Internal digital transformation initiatives and access to new technology such as cloud computing, machine learning and microservices made it possible to shift towards a scalable ingestion platform.

A Case Study of Understanding Bonaparte Basin using Unstructured Data Analysis with Machine Learning Techniques

EAGE, Australia | 2021

The objective of this study is to understand and obtain meaningful insights into the Bonaparte Basin based on the substantial amount of information available in previous studies, reports and presentations. The unstructured data of Bonaparte Basin have been ingested in a Knowledge Container through consecutive ML and AI pipelines and analysed using big data analytics tools.

Supporting the UN 2050 Net Zero goal by reading the earth better

Learn more in the Iraya Energies article “Supporting the UN 2050 Net Zero goal by reading the earth better” in the First Break June 2021 issue by N. M. Hernandez, K. G. Maver, and C. Mamador.

If you would like a copy of the article, please send an email to info@irayaenergies.com

APPLYING AI TO UNSTRUCTURED DATA FOR FASTER AND BETTER E&P DECISIONS? 

Learn more in the Iraya Energies article “Accelerating E&P decisions by applying AI to unstructured data” in the First Break December 2020 issue by K. G. Maver, C. Mamador and F. Baillard.

If you would like a copy of the article, please send an email to info@irayaenergies.com

YOUR SUCCESS MIGHT BE LIMITED BY YOUR INABILITY TO FULLY LEVERAGE YOUR DATA

Oil and gas companies are awash with data, the amount of data is growing exponentially and 80% is estimated to be unstructured and therefore to a large extent unusable. Less than 1% of the collected data are used by oil and gas companies in decision making.

Learn more about unstructured data and how artificial intelligence can help in the white paper “Bringing Unstructured Engineering and Geoscience Data into a Fully Digitized World for Information Extraction (IE)”.

EXPLORING UNSTRUCTURED E&P DATA CAN REVEAL NEW VALUE

17 June 2020

Digitization within the oil and gas industry are creating substantial efficiency and commercial gains, which you can read more about in our newest article “Exploring Unstructured E&P Data Can Reveal New Value” by Kim Gunn Maver, Charmyne Mamador, and Nina Marie Hernandez, which has been posted on DSDE: https://pubs.spe.org/en/dsde/dsde-article-detail-page/?art=7216

HOW CAN YOU INSTANTLY EXTRACT INFORMATION FROM UNSTRUCTURED DATA?

Learn more in the Iraya Energies article “Processing of unstructured geoscience and engineering information for instant access and extraction of new knowledge” in the First Break June 2020 issue by K. G. Maver, N. M. Hernandez, F. Baillard, and R. Cooper.

If you would like a copy of the article, please send an email to info@irayaenergies.com

DIGEX2020: EXPLORATION HISTORY AT YOUR FINGERTIPS

Oslo, Norway | January 28-29, 2020

In new ventures efforts it usually takes months or even years to fully understand and grasp the drilling history and geoscience. Using a wolfpack analogue decades for exploration history is instantly accessible in the ElasticDocs Intuitive Knowledge Container.

A geological regional case study for pressure, temperature, and salinity for the Gulf of Mexico using machine learning technology on unstructured data

AAPG Digital Subsurface | 2020

Traditionally explorationist working on a new area are given a huge amount of the data and will start with a regional study to identify plays and reservoirs on a basin scale. Once opportunities are identified, an area will be selected, and the study will move into a block scale.

A Case Study of Fully Automated Machine Learning Petrophysical Interpretation Using Unstructured Data

AAPG Digital Subsurface | 2020

Machine Learning (ML) has become widely used for regional studies and prediction of petrophysical
interpretation and facies classification. In recent years, application of ML has shown significant benefit to improve productivity, decision making and success rate for regional exploration campaigns.

Why People matter in a succesful digitalisation strategy

APGCE Kuala Lumpur, Malaysia | 2019

To be successful, it is necessary for the digitalization effort to look across and meet the various
business, technical, and cultural needs of the stakeholders within the organization. To identify
digitalization projects that bring the most value, we should ask three main questions: 1) the
desirability: what do people want, and need? 2) the feasibility: what can the people do? 3) the
viability: how can people succeed?

A New Way of Handling Unstructured Data in The Age of Digitalization

Manama, Bahrain | 2019

Years of field development and production have generated huge amount of data produced by different G&G disciplines. Any operators will therefore be facing during the field lifetime common challenges such as 1. An exponential amount of data created 2. A variety of document type and format being generated 3.A retention of knowledge from senior personal leaving the company.

PETRONAS AND IRAYA ENERGIES PRESENTING AT APGCE

Kuala Lumpur | October 29 – 30, 2019

• Why people matter in a successful digitalisation strategy

• Rock Physics At-Scale, enabled by Big Data Analytics & Machine Learning

FORCE SEMINAR: PACK OF WOLVES STRATEGY

Kuala Lumpur, Malaysia | September 20, 2019

• Learn more about how an effective G&G exploration strategy can be inspired by a wolfpack.

EAGE BIG DATA AND MACHINE LEARNING CONFERENCE

Kuala Lumpur, Malaysia | February 25 – 27, 2019

Value Proposition of ElasticDocs Data Management – How can Data Analytics and Machine Learning Techniques Help Companies Reveal Hidden Trends in a New Ventures Effort?