Uncertainty management: A structured approach towards recognizing, quantifying and managing subsurface unknowns
- Laurent Didier Alessio (LEAP Energy Partners) | Arnout J.W. Everts (Murphy Sarawak Oil Co. Ltd.) | Faeez Rahmat (LEAP Energy Partners)
- Document ID
- Society of Petroleum Engineers
- SPE Asia Pacific Oil and Gas Conference and Exhibition, 18-20 October, Brisbane, Queensland, Australia
- Publication Date
- Document Type
- Conference Paper
- 2010. Society of Petroleum Engineers
- 7.6.6 Artificial Intelligence, 5.8.3 Coal Seam Gas, 5.1.5 Geologic Modeling, 5.5.2 Core Analysis, 3.3.6 Integrated Modeling, 5.1.2 Faults and Fracture Characterisation, 5.1.8 Seismic Modelling, 5.5.8 History Matching, 5.8.7 Carbonate Reservoir, 7.1.5 Portfolio Analysis, Management and Optimization, 5.6.4 Drillstem/Well Testing, 4.3.4 Scale, 1.1 Well Planning, 1.6 Drilling Operations, 1.12.3 Mud logging / Surface Measurements
- 0 in the last 30 days
- 388 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
This paper illustrates, through field studies examples, why and how a structured approach towards managing uncertainties, and especially sampling biases, delivers valuable insights through the successive early asset life stages - exploration, appraisal and field development phases. In doing so, we respond to three fundamental questions.
Firstly, ‘What are the key uncertainties - those that matter?' Field studies should begin with a comprehensive upfront assessment of uncertainties' impact on historical and future well and field performance. However, often major factors are overlooked, leading to under-prediction of true outcome ranges and the inability to reconcile historical production. Our illustration is a large producing carbonate field, where after 15 years of production, large scale Karstification was finally evidenced to be the explanation for the field performance that couldn't be history matched with the measured matrix porosity and permeability ranges.
Secondly ‘What are realistic ranges for these uncertainties?' Known Industry best practices include intensive expert-assist, integration of drilling, mud-logging
and other traditional sources of data from the field, resorting to analogue benchmarking. Despite these, we often fail to understand and correct for sampling bias,
which we show often leads to over-optimism. The paper will highlight why such biases are present and propose simple and practical methods to remove them. The case study is the volumetric assessment of a gas discoveries portfolio, where geophysical techniques were instrumental in exploration and appraisal drilling.
Finally ‘How these uncertainties will evolve with time?' This is an important question for assessing value of Information: the impact that additional data may have on the uncertainty range of uncertainties and the base case. Unconventional fractured plays, often characterized by data abundance but extreme variability, provide surprising insights on how uncertainties ranges evolve. This paper presents methods to develop confidence curves for important parameters.
This paper illustrates, through field studies examples, why and how a structured approach towards managing sampling biases in reservoir evaluation delivers valuable insights through the successive early asset life stages - exploration, appraisal and field development phases.
Whilst the purpose of the paper is not to provide a comprehensive review of uncertainty management best practices, a workflow and fundamental steps are discussed herein, to provide some contextual framework. We then focus our illustration around identification of key uncertainties, defining realistic ranges for these, and finally assessing how ranges should evolve with time.
|File Size||560 KB||Number of Pages||13|