Agenda

While huge discoveries are becoming rare, the industry is under pressure to explore challenging frontiers. In addition to conventional processes and tools, understanding the discovered fields of the basin (its fluid distribution, porosity/reservoir quality preservation, rock physics, tectonic settings, depositional settings) at different scales with a lens of innovation and ingenuity is imperative. Furthermore, the pressure on widespread reduction of exploration budget is quite demanding to defend the costs.

The session will showcase case studies highlighting best practices, success stories, and lessons learnt in reservoir characterisation catered to the needs of the business in the recent challenging times. The session will also discuss innovations in data acquisition, improvement on its quality, the value of the information, and the processing/interpretation of integration along the field life cycle, spanning from exploration to development. 

This session will feature discussion on innovative workflows facilitating the integration of large number of available data at different scales, originating from various disciplines including geophysics, geology and petrophysics. With the increase of data gathering such as 4D and 4C seismic, seismic monitoring for specialised well logs, non-seismic survey, production data and so on, it is essential to consolidate all this information into one consistent subsurface model.

Several challenges must be addressed to build such a model to further understand the reservoir, with respect to associated uncertainties. Scientists from processing to reservoir engineering need to identify the limitations/uncertainties related to the data and models, and understand the process of geological modelling (including structural modelling, facies modelling, property modelling and upscaling) to ensure that a model update is comprehensive and encompasses all disciplines, and  should not compromise the underlying hypotheses of any previous models.

Furthermore, new tools like machine learning may be needed, as more diverse data are available for reservoir characterisation and conventional approaches (statistics/geostatistics) may not be sufficient. The integrated workflow will focus on different topics, including:

• Seismic QI, reprocessing and inversion – Improvements and value to the reservoir
• 4D and 4C seismic
• Improved geological modelling methodologies – Exploration
• Improved geological modelling methodologies – Production
• Artificial Intelligence (AI), machine learning (ML), big data analytics, cloud computing
• Case studies – Success stories or lessons learnt

For many years, there has been a common understanding in the industry that integration of modelling efforts by all disciplines is the next step to bring the quality of reservoir descriptions to the next level, where predictiveness and realistic confidence estimates on those predictions are greatly improved.

However, the domain-silo approaches of the last century remain. Why and what can be done to encourage and ensure integrated modelling? Is it work processes, software, or both? Is the shift towards integration as the standard finally happening during this current turmoil of our industry?

This session will address integrated modelling by providing some deep-dive examples of actual achievements in integrating modelling approaches from a variety of disciplines, to stimulate discussions on the “New Era of Integrated Modelling”. Discussion topics may include:

• Full-cycle integration in reservoir modelling
• Subsurface and surface integration modelling
• Dynamic and geomechanics coupling
• New platforms and environments for speed – Cloud computing
• Case studies – Success stories or lessons learnt

Despite years of development and production of various petroleum basins around the world, effective reservoir characterisation required to produce more accurate production forecast is still a widely known challenge for geoscientists and engineers. Accurate reservoir characterisation brings tremendous impact to the commercial success of an asset in the low oil price environment, from exploration to production. This is especially pronounced in complex reservoirs, whereby the performance cannot be accurately predicted without accurate reservoir description.

Complex reservoirs such as thin beds, unconventionals, fractured reservoirs, and carbonates often face challenging reservoir characterisation. Considering the complexity of these reservoirs, characterisation often requires extra data and resources compared to conventional reservoirs. However, with the advancement of technology and increased collaboration between multiple disciplines, proper characterisation in these challenging reservoirs can be economically rewarding.

This session will focus on new tools, methodologies, insights, and best practices for reservoir characterisation in these challenging settings.

Reservoir simulation is based on well-known reservoir engineering equations and techniques that have been used for years. Our concern is how to create a robust model to describe reservoirs’ performance under geological uncertainties in various operating conditions. Although simulation in the petroleum industry is not new, many novel techniques are being continuously developed to detail reservoir features and better history match of flow dynamics. These complex modelling are becoming practical due of the capability afforded by available powerful computers and software. 

This session will discuss recent innovations, latest technologies, and case studies in the modern reservoir simulations. Related topics may include:

• New and improve workflow
• AI/ML/Big data analytics/Cloud computing/Open Subsurface Data Universe (OSDU)/Central Processing Unit (CPU) and Graphics Processing Unit (GPU) capability
• Ensemble modelling
• History matching and uncertainty analysis – How far we should go?
• Conventional (bottom-up) modelling vs. top down modelling
• Case studies – Success stories or lessons learnt

One of the key factors of a successful integrated field development project is well-planned laboratory work. A well-defined and successful implementation of any laboratory work can be narrowed down to the range of uncertainties that exists in a project. The requirements for extensive laboratory work to support the study has increased with the rising level of the project complexities.

Converting laboratory results to meaningful interpretations and representations of a field is also another aspect that an asset team must focus on. Otherwise, the expensive laboratory work investment will be wasted.

This session will discuss the key components of laboratory works in field development covering all aspects from key data requirements, best practices, challenges and the latest innovation. This session will combine various perspectives from oil and gas operators and service providers based on related topics below:

• Key data for reservoir characterisation
• Sample validation, QA/QC of laboratory reports
• Upscaling challenges - Lab results to field deployment
• EOR Pilots in economic scale
• Case studies – Success stories or lessons learnt