The SPE Queensland Section Annual General Meeting for 2022 will be held on Friday April 1st, 2022, in conjunction with the Distinguished Lecturer Series Luncheon on the same day.
The AGM and the Lecturer Series will occur between 12 and 2 pm at the Tattersall’s Club, 215 Queen St, Brisbane City QLD 4000. For booking, please visit the event’s page.
The SPE Queensland Board is looking forward to meeting you soon.
Peter Nigel Cameron
Energy Markets Global Limited, UK
Abstract:
The global Liquefied Natural Gas (LNG) industry is going through disruptive transformation. This term is often heard but one we can now see applied to a large capital intensive industry. For decades, the LNG business had been traditional, cosy, conservative and uncompetitive, with a few large players, opaque and rigid.
We consider the drivers that make an industry change, particularly an energy industry and the characteristics of energy markets. We find that LNG meets the criteria of an industry ready for change. Three key factors drive transformation, namely an industry wide crisis, action to enable the business environment and then the market developing by itself.
Drivers for LNG change include technological (FSRU, FLNG, supersized ships, Mini-LNG, bunkering) and commercial (global LNG oversupply, regulatory, new demand and supply centres, new business models, new players, new trading patterns). Any of these can provide an industry crisis but among the most important is the entry of the USA, set to become the world’s marginal LNG supplier (which sets the price) and also largest supplier, with different business models. The combined effects are transformational.
Change is continuing, with the growing influence of the USA, LNG hubs, bunkering and mini LNG. The sleepy traditional LNG world is changing and will continue to do so. Coronavirus has a large impact but the LNG world was already transforming.
Members should consider what further disruption will come. They should also identify additional value in gas from previously stranded supplies and demand which can now be opened up.
Biography:
Mr Peter Cameron is Founder and Managing Director of Energy Markets Global, a UK based international energy markets consultancy. He is also lecturer in Business Strategy for Middlesex University (UK) Oil and Gas MBA.
Peter advises governments and companies globally on the economic and commercial aspects of gas markets. He has over 25 years of worldwide project experience (long and short term). He was Chairman of the International Services Board of the Society of British Gas Industries and represented the British Government on the gas sector worldwide. Previously, Peter was Corporate Strategist and Planner for British Gas (now Shell).
River Room, Customs House from 5.00pm
MORNING TEA Tuesday November 16 – 8am free to conference attendees
Biography:
Mark Stone is the Chief Executive Officer of Resources Safety and Health Queensland, the independent regulator of safety and health in the explosives, mining, quarrying and petroleum sector.
Before joining the Queensland Government in 2014, he spent 20 years in the international oil and gas industry, in upstream technical and leadership roles. For most of that time he has been a member of the SPE, serving on global and regional committees. Mark holds a master’s degree in Petroleum Engineering and is a Fellow of the Institute of Engineers Australia.
Summary:
RSHQ’s vision is zero serious harm across the state’s resources sector. Our mission is to regulate, educate and assist industry in meeting its obligations to protect and promote the safety and health of persons from risks associated with mining, quarrying, explosives and petroleum and gas. In this presentation, Mark will discuss what it means to be a risk-based regulator, how the Queensland resources sector is performing, and the role we all must play in achieving zero harm to workers.
Biography:
Darren is the Project Director at CTSCo. CTSCo is the proponent for a Surat Basin carbon capture and storage project seeking to reduce emissions from power generation in Queensland.
Darren has over 22 years of oil and gas exploration, development and operations experience including previous executive roles with Senex Energy and the drilling and energy services companies Easternwell and High Arctic Energy Services. Darren holds a Bachelor of Mechanical Engineering degree from the Queensland University of Technology and is a Chartered Engineer and Engineering Executive with Engineers Australia.
Topic Summary:
CTSCo’s Surat Basin Carbon Capture and Storage Project
The objective of the Surat Basin carbon capture and storage (CCS) project is to demonstrate the viability of carbon capture from a coal-fired power station and the effective permanent storage or use of the captured CO2. The project is intended as a first step toward large scale CCS within a Surat Basin hub, with emissions from multiple generators and other industrial sources being captured and safely stored. The CTSCo project comprises a proposed demonstration-scale CO2 capture plant and a CO2 sequestration site located in the southern Surat Basin.
To date, CTSCo has investigated several potential storage reservoirs located close to CO2 sources in central Queensland and are currently focused on the appraisal of a site located in the southern Surat Basin with industrial-scale sequestration potential. As part of the project, CTSCO has initiated and supported a range of research and development projects associated with the monitoring and verification of injected CO2, storage reservoir evaluation, containment de-risking and environmental monitoring.
There is an upcoming Online Data Analytics bootcamp that PESA QLD are offering (run by Halliburton).
The course will be five 3 hr online sessions across the week starting 9th August. I have included the session overview below (more details in link).
| Presentation: | Data Science E&P Bootcamp Online Course 2021 |
| Venue: | Participants will be provided with a MS Team link. Trainers will be broadcasting from Brisbane/International. |
| Date & Time: | Session 1 – Mon Aug 9, 1:00-5:00pm (AEST) Session 2 – Tue Aug 10, 1:00-4:00pm (AEST) Session 3 – Wed Aug 11, 1:00-4:00pm (AEST) Session 4 – Thu Aug 12, 1:00-4:00pm (AEST) Session 5 – Fri Aug 13, 1:00-4:00pm (AEST) |
| Cost: | PESA Members: $950.00Non- Members: $1150.00
Student/Retired Members (max 2): $750.00 |
https://pesa.com.au/events/pesa-qld-online-course-data-science-ep-bootcamp-2021/?mc_cid=1815fb91d8&mc_eid=bb52cd3bd7
Data Science E&P Bootcamp Online Course 2021
Session #1: General Theories of Data Science Including O&G Case Studies and Basic Theories of Artificial Neural Networks (4 hours)
This session will introduce the participants to data sciences, machine learning and artificial intelligence in the light of energy industry. The focus will be on the concepts of different machine learning techniques and algorithms in general. Additionally, a few real case studies applicable to solve energy industry problems will be discussed to make participants understand the concepts. Also, the session will discuss artificial neural networks (ANN) concepts to the participants, as ANN finds numerous applications for AI/ML driven solutions implementation. This session caters the conceptual understanding of different architectures of neural networks, namely, CNN, RNN, MLP, etc.
Session #2: Exercise – Facies Classification trough Data Driven Well Log Analysis (3 hours)
Supervised / Unsupervised approach will be used in this hands-on exercise for facies interpretation using well log data. Facies classification finds a valuable application to determine the reservoir or non-reservoir facies. Data driven approach will emphasise the value addition besides conventional petro-physical approach of solving a similar problem.
Session #3: Exercise – Well Log prediction from Seismic Attributes (3 hours)
A supervised machine learning technology will be used for synthetic log generation in this exercise. A novel approach will be discussed in this exercise, so the participants can generate their own synthetic log. These logs can be used further for static reservoir modelling. This exercise will also help the participants to understand the hyper-parameters of model tuning aspects.
Session #4: Exercise – Fossil Classification through Computer Vision Image Analysis (3 hours)
Computer vision will be used to do some image analysis of fossils. This supervised method can be very useful for the participants for implement the similar model for well core sections for identifying biomarkers or do bio-stratigraphic interpretation.
Session #5: Exercise – Short Term Production Prediction through Machine Learning Model (3 hours)
Prediction of the oil or gas production is the key for successful hydro-carbon business. Machine learning based approach finds a very good application in this scenario. In this exercise, the participants will earn about the concept of time series analysis and ANN will be used to predict the gas rate for time series data.
ABSTRACT:
Our industry is facing mounting pressure from all sides to decarbonize. Regulations are getting tougher in response to public pressure and investors are pulling capital out of the oil and gas industry over concerns that assets may be left in the ground stranded. Natural gas has been touted as the transition fuel to net zero. However, because of the methane emitted in the production and distribution process, some believe that it is dirtier than coal and cannot play this role. We have to act now or our industry will be viewed adversely and collectively we will make poor energy decisions for our planet. Technology that is able to detect and measure methane emissions is showing that we have a problem along our entire exploration, production, processing and distribution chain. According to the World Bank Global Gas Flaring Reduction group, every day over 14.5 billion standard cubic feet of gas is vented and flared globally. In addition to being a monumental waste of energy, this practice releases methane, VOC’s, PAH’s, particulates and other harmful pollutants into our atmosphere impacting air quality, human health and contributing to climate change. Methane is 86 times worse that carbon dioxide over a thirty-year time frame which means that eliminating these fugitive emissions is one of the biggest opportunities to cost effectively reduce greenhouse gas emissions providing an immediate impact in a short time frame. Can we act, do we have time to change the narrative and can we create a great future for all? What will it take?
BIOGRAPHY:
Audrey Mascarenhas is President and CEO of Questor Technology Inc. (Questor) a public, international company focused on clean air and energy efficiency technology. Audrey has worked in energy and environment for over 39 years with Gulf Canada Resources Ltd., presently Conoco-Phillips and Questor. Through her leadership, she has focused on technology solutions to eliminate methane and harmful pollutants. Audrey shares her passion for the environment and served as a distinguished lecturer with the Society of Petroleum Engineers (SPE) in 2010-2011. She is a graduate of the University of Toronto with a Bachelor’s degree in Chemical Engineering and holds a Masters Degree in Petroleum Engineering from the University of Calgary. She is a fellow of the Canadian Academy of Engineers. Audrey was the recipient of the Ernst & Young Entrepreneur of the Year 2011 Prairies Award for Cleantech and Environmental Services and received a national citation for Values[1]Based Innovation. Audrey currently serves as an expert on the Circular Economic Panel advising Environment Canada, the Lazaridis Institute global advisory and the Schulich Industry Engineering Advisory Council. She chaired the Canadian Federal Government Clean Technology Economic Strategy table and was an Energy Futures Lab fellow.
Abstract:
Subsurface Analytics is an alternative to traditional reservoir modeling and res. management.
Positively influencing subsurface related decision making is the most important contribution of any new technology. Subsurface Analytics is the application of Artificial Intelligence and Machine Learning (AI&ML) in Reservoir Engineering, Characterization, Modeling, and Management. Applicable to both conventional and unconventional plays, Subsurface Analytics goes far beyond the traditional statistical algorithms that use only production data and fail to take into consideration the important field measurements such as well trajectories, well logs, seismic, core data, PVT, well test, completion, and operational constraints. Subsurface Analytics is the manifestation of Digital Transformation in Reservoir Engineering, Modeling, and Management.
Subsurface Analytics is a new and innovative technology that has been tested and validated in a large number of real life cases in North and Central America, North Sea, Middle East, and Southeast Asia. It has been successfully applied in several highly complex mature fields where conventional commercial reservoir simulators were unable to simultanuously history match multiple dynamic variables for large number of wells. Results and field validations from multiple case studies are included in the presentation.
Subsurface Analytics addresses realistic and useful applications of AI&ML in the upstream Exploration and Production Industry. The technology has been validated and confirmed for (a) prediction of well behavior under different operational conditions, (b) modeling and forecasting pressure and saturation distribution throughout the reservoir, (c) infill well location optimization for both producers and injectors, (d) choke optimization for production improvement, and (e) completion optimization for production enhancement.
Biography:
Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum Engineering at West Virginia University and founder of Intelligent Solutions, Inc. He has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He has been featured as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2004). He is the founder of SPE’s Petroleum Data-Driven Analytics Technical Section. He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon and was a member of U.S. Secretary of Energy’s Technical Advisory Committee (2008-2014). He represented the United States at ISO on Carbon Capture and Storage (2014-2016).
Extracts from URTEC 2790, SPE 2020 Workshop on Rate/Pressure Transient Analysis and Distinguished Lecturer Presentation 2019/20
Lawrence CAMILLERI
With the increasing volatility in the price of oil, even low-cost onshore wells can be economically marginal and not achieve target rates of return under certain oil price scenarios and P10 or even P50 production profile forecasts. If such market conditions persist, it will become increasingly important that all wells produce to their full potential and that a war be waged on skin and that drainage area be maximised. Where wells are fractured, maximising fracture conductivity and half-length will also be essential. It is the author’s belief that this macro-economic environment will drive an increase in pressure transient analysis (PTA) complemented by rate transient analysis (RTA) because, just as Lord kelvin stated in 1883, what we cannot measure, we cannot improve.
Traditionally PTA requires build-ups, which is costly, both in terms of intervention and deferred production. Fortunately, the population of permanent downhole gauges does not cease to increase and this presentation examines how downhole real time pressure and rate data enables PTA in drawdown. Such technology not only provides a source of low-cost transient data, but also the ability to obtain inflow characterisation (mobility, skin, fracture half-length and conductivity and boundary conditions)) within a few weeks of completing a well, thereby confirming the time value of information.
To illustrate the concepts, PTA and RTA results from a MFHW (Multi Fractured Horizontal Well) case study are reviewed. This is based on data taken from URTEC 2790 paper and is particularly interesting as it demonstrates how high-quality production data can resolve a large number of unknowns. Also the history matching of flowing pressure with an analytical model yielded excellent results despite many discreet changes in flow-rate, which further enhanced confidence in the inflow characterization. Therefore if PTA can be performed in drawdown on a MFHW in unconventional tight formations, it should be relatively easy to achieve on conventional vertical wells. This workflow was applied to over 38 wells in the Bakken to generate a correlation of PV (Pore Volume) and PI ( Productivity Index) as a function of completion and fracture design thereby providing quantifiable feedback on how to drill and complete “winning” wells in a particular geological environment.
As always, economic constraints often stimulate technological creativity, and the author predicts that it will not be long before PTA in drawdown is industrialised and applied to SRP and free flowing wells with the addition of a downhole tools such as a venturis. Can we conclude that, even in a low oil price environment, economic production is possible if production is optimised utilising the right instrumentation and interpretation techniques ?
Biography:
Lawrence has over 37 years of experience in production operations of which 27 years have been focused on artificial lift and production optimisation in a variety of roles ranging from field and application engineer to Global Artificial Lift Domain Head for Schlumberger. He has recently founded his own consultancy business (Camilleri & Associates) focussing on production optimisation. He has published over 19 SPE conference papers and 5 patents covering all aspects of ESP operation such as inflow characterisation and advanced completions and was SPE Distinguished Lecturer for 2019/2020 on the topic of production optimisation. This body of work is particularly noteworthy as it combines theoretical explanations and field case studies using real time data.
