Tuesday, May 16
The panel discussion will be opened by an introduction and brief presentations of the panelists as starting point for this Applied Technology Workshop to discuss the role of data management as enabler for Operational Excellence. You will hear about the current status in other industries and be able to compare with the oil and gas industry. The following moderated discussion will the focus on learnings derived in the past to identify gaps in technology and process and how a way forward could look like.
Operational Excellence, as management philosophy and operating model, strives to continually create value for customers, employees and key stakeholders by improving operations and business performance through a holistic, standardized management methodology and framework across the entire organization. A combination of setting clear goals, monitoring the implementation and results to deduct learnings determines the actions required to achieve operational excellence – usually related to people and leadership.
But, how does “Big Data” play a role here? A number of questions arise:
- We seem to have a massive amount of data, but how are we using them?
- Can we be satisfied to collect data to administrate our work or do we do that with improvement in mind?
- Who are the users of data and processing results?
- Is data QA/QC’d and fit for this purpose?
- How can operations and IT cooperate to find solutions adopting to business needs in an agile way?
- Why does data collection and processing seem to be an issue at times where computing speed, data storage etc. is not a challenge by no means.
We trust that our panelists will provide insight from different industry's perspectives and expect a lively discussion, which identifies opportunities, gaps and how the oil and gas industry can adopt to close these gaps. We will learn from other industries, or may discover that we already have examples of best practices within the oil and gas industry.
This panel discussion will be the start to a successful workshop where more details around “Data Enabled Operational Excellence – The Value of Information in Oilfield Operations” will be discussed.
- Prof. Gerhard Thonhauser, Head of Department Petroleum Engineering at Montanuniversitaet Leoben
- Dr. Jörg Simon, Enterprise Architect, TenneT
- Markus Berghofer, Head of Information Management and Business Projects, OMV
- Thomas Friedmann, Process Industries and Drives Division, Siemens
- Andreas Schuchardt, Information Services Governance, Wintershall
Digital Oilfield projects have been deployed over the last 15 years, and yet the main obstacles to make such strategy sustainable are organizational and cultural issues. The explosion of data and the continued evolution of data analytics capabilities will require changes in the culture in how we will work in Digital Oilfields in the future. Established work processes cannot simply be overloaded with even more data while management expects better and faster decisions.
This session addresses the opportunities in data enabled operational excellence but also the lessons learnt from the past why agile decision making has not been achieved by simply providing more data to engineers, operators and managers. What will be the challenges for our organizations when even more digitalization in our business processes bring much more data?
How does the roadmap for a journey look like, that organizations will need to take to stay relevant in the era of agile decision making? How does an operational excellence vision look like which harmonizes analytic power, human capital, processes and technology? What is needed with regard to governance, change management and culture change?
We would like to encourage papers and presentations of lessons learnt and concepts of how cultural changes need to be planned and implemented to achieve operational excellence and turn the overload of data into a competitive capability.
- Mario Dreier, Head Of Department Productions Operations & Torsten Helbig, Business Planning & Support, Wintershall
The advent of the Internet of Things, and advancements in data gathering and infrastructure have been enormous in recent years, delivering a tsunami of data to a reduced body of human resources across a range of E&P departments. Traditional methods of data and information management are no longer sufficient to extract the information that will drive business transformation. However, in line with the increase in data, a step change in information technology and data science is creating new opportunities to harness and add extract value from data. Increasingly, manual extraction of data is insufficient, but operators are now embracing integration from the subsurface to the head office, accelerating decision making, improving execution and increasing efficiency.
This session will focus on the key infrastructure elements to gather, assure and store data, and the latest tools for extraction of business value to enable processes and workflows to drive efficiency improvements, in the form of vizualization, integrated workflows and analytics.
Michael Scott, IOS Regional Practice Manager, ESSA - Landmark/Halliburton
Steve Eacott, Production Optimisation Lead, Accenture
Industry 4.0 has arrived in the oil and gas sector. This session explores how digital technology is changing the landscape – for the better. Digital continues to disrupt existing business models and organizations in today’s world of compressed margins. Small companies are gearing up to challenge the big players in the market by using innovative ways to extract maximum value from data. E&P companies are pressured more than ever to make the leap from the original design of Digital Oil Field to its full implementation in order to position themselves for the future. Recent examples of value realization from digital projects in on- and offshore, green and mature environment, will be shared and discussed. The impact of real value to data in scope of Integrated Operations across Upstream, Downstream and companies, such as utilities, will be explored using the maturity model and IO value pyramid. And “Real Value” means not only financials, but business relevancy and organizational culture.
This organizational change will be also the underlying theme in this session, based on cross-industry learnings and the newest methodologies in (Big) Data Analytics.
Dr. Jörg Simon, Enterprise Architect, TenneT
Johnny Gipson, Senior Industry Consultant, Oil & Gas, Teradata
Philipp Tippel, Head of Asset, OMV Petrom - Smart and Automated Workover Candidate Selection
Wednesday, May 17
How can history guide us into the future? Digital Oilfield (DOF) may seem as a recent concept in the E&P Industry, but it has in fact evolved over several decades where online field data increasingly have become more available and utilised in operations. There has been huge amount of lessons learned from this process and many of them were really though ones. This gives us the opportunity to connect negative and positive insights from past to create new knowledge and modify existing practices. This can increase our capacity to ensure the potential success of the present and future development of the DOF.
Regretfully there are indications that we currently are not tapping the full potential from these lessons. One example is that we can already see from the historical trends that huge part of the challenges and business opportunities have been on the people side of the business; management of change, need for culture change at all levels, personnel skill development, new design of business processes, and communication of the value proposition to be gained. But, still it seems that many DOF projects and initiatives are managed mainly as infrastructure projects with a pure technology focus, typically rooted in the technical functions of the organizations, and is finally evaluated by their technical success only!
In this session, lessons learnt from previous operational excellence programs shall be openly discussed. Did data truly enable and contribute to operational excellence? What worked and what not? How were operational excellence programs implemented? What was the role of management in these programs? Was DOF strategy and operational excellence vision aligned?
How was a standard defined for requirements, implementation methodology, technical guidance, and audits? Did companies achieve operational excellence objectives like modifying operational practices to take DOF technology into account, framing the DOF to optimize its value, and feeding operational excellence with reliable data to maximize decision quality? Did the combination of DOF and operational excellence programs achieve the integration of disciplines and groups to do integrated data analysis of special projects or when major failures or deviations occurred?
- Mario Dreier, Head Of Department Productions Operations & Torsten Helbig, Business Planning & Support, Wintershall
Business Information Management is a strategic area in order to successfully carry out E&P activities. All the information generated during the exploration to production processes and the supporting processes are among the organization´s most valuable assets. The high and continuing increase of data and documents used by a growing number of contributors in the different disciplines and locations of the company is deeply impacting E&P activities. The company´s business information management processes, rules, governance models and tools have therefore to evolve efficiently to sustain the company´s development. Hence studies and projects have been launched to achieve the goals accordingly.
There are many forms of case studies. In the E&P business world case studies mostly describe a success story used to promote a product or the company providing solutions. The case studies which are expected to be presented in this session should describe specific company problems and how the company strategically approached these issues. Ideally the study results succeeded in fixed standards and workflows. Independent from the outcome of the study it is of interest to discuss the lessons learned with the pros and cons of the approach together with tentative recommendations on how the data could be managed better.
- Ulrich Bülow, ENGIE - Case study: In search of an effective management of geoscience data. An approach with the analysis of the data handling process within ENGIE in Norway.
- Uwe Wittendorfer, Senior Account Manager, Cloud-based Optimization of Control Performance / Experiences from other industries, Siemens AG
- Dr. Hans-Joachim Wallrabe-Adams, Data management in IODP – ECORD Science Operator, University of Bremen
The boom for collecting and storing data is still overwhelming but an open question is still, which data and technologies do we really need for an effective monitoring and optimisation of processes? Do we have enough data or is the still increasing process of allocating more and more storage capacity poppycock? Are machine learning, soft computing and artificial intelligence providing us with suitable tools or is it an overkill due to a general hype? Is knowledge extraction from data just a buzzword or is it really moving our industry forward to gain a competitive edge?
This session addresses the future trends and potentials ranging from data acquisition and storage via data QC and cleansing to self-acting inference.
New storage technologies can help to access the myriads of data in an efficient and easy recoverable way. Data QC is still an undisputed challenge and visual analytics might at all lead to understanding of what information is in the data. Machine learning tools help to create models without knowledge of the underlying physics and probabilistic models may give some better understanding of the fuzziness of things due to data uncertainty.
Papers dealing with such subjects are welcome in the session regardless whether they contain pros or cons for big data and intelligent analysis methods.
- Andreas Harrer, Robert Walitsch, HOT Engineering GmbH - Does Sole Optimization of Objective Functions yield Predictive Reservoir Models?
- Nirtl Michael, Montanuniversität Leoben; Kometer Bernd, OMV Exploration & Production GmbH - A New Approach to ESP Sensor Data Analysis
- Abbas Zamani, Montanuniversitaet Leoben; Christian Burgstaller, RAG - A Deep Learning Approach to Production Data Analysis