Pei, Peng (Department of Geology and Geological Engineering, University of North Dakota) | Zeng, Zhengwen (Department of Geology and Geological Engineering, University of North Dakota) | Liu, Hong (Department of Geology and Geological Engineering, University of North Dakota) | Ahmed, Salowah (Department of Geology and Geological Engineering, University of North Dakota)
Ideally, geoscientists would like to have quantitative information about rock properties, along with information about fluid content of potential reservoirs relatively directly from the seismic as this information is available as oppose to the well data. Historically, seismic images have stopped short of delivering this, as the seismic bandwidth was limited due to the conventional streamer design and acquisition method.
The ability to predict reservoir properties away from the well using seismic information is a key element in quantitative interpretation. Quantitative seismic interpretation combines various types of data: well, seismic and seismic interpretation or geological prior information. Thus, this workflow is integrated and the quality and accuracy of each individual constituent is of great importance to the accurately estimate the volume of hydrocarbon in place in a particular reservoir interval.
Seismic plays a key role in this, and if the seismic data contains very strong low frequency information and the seismic image is of high quality/resolution, it is possible to directly estimate the absolute impedance at each point within a seismic volume.
Over the last few years, new acquisition methods and technologies exist aiming to provide a broader seismic bandwidth: streamer towed shallow at the front and going deeper at the mid of the streamer, towed acquisition with some streamers at shallow and deeper depth, and the dual-sensor towed streamer.
These new broadband seismic data volumes are bringing the seismic a step closer to the reservoir and this is what we will try to demonstrate in this presentation. We will have the latest look at some of the newest and most exciting improvements in reliably unraveling the rock properties from the 3D seismic data.
Probabilistic methods for reserves estimation, including uncertainty quantification and probabilistic aggregation, have gained widespread acceptance in the oil and gas industry, since the first comprehensive guidelines were issued by the Society of Petroleum Engineers (SPE) in 2001. The probabilistic methods now used in the oil industry, as proposed in these guidelines, are similar to those also used in portfolio theory and risk management by the finance industry. A significant amount can be learned from the extensive experience with probabilistic methods and quantification of risk with measures [e.g., value-at-risk (VAR)] in financial risk management. Especially, the guidelines issued by the Basel II Accord (Bank for International Settlements 2006) and the discussions since the 2008 financial crisis contain important lessons.
In this paper, we examine a fundamental question: "Is the P90 reserves value an appropriate measure for quantifying the reserves' downside?" For the P90 reserves value to be considered a good measure of the reserves' downside, it needs to possess a number of basic characteristics involving P90 reserves for each field and the probabilistically aggregated P90 reserves for the portfolio of fields. Analogous to the definition of a coherent risk measure used in the finance industry, we define these characteristics for P90 reserves.
The P90 reserves are as good a risk measure as VAR used in the financial industry. However, like VAR, it is not a coherent risk measure. A possible uncertainty scenario, in which one of these necessary characteristics does not hold, is given. An alternative measure of risk for quantifying the reserves' downside, defined as the average reserves over the confidence interval higher than P90, is presented. This is a coherent risk measure.
In this paper, we highlight the appropriateness and limitations of using the P90 reserves estimate as a measure of the reserves' downside. Understanding of the limitations posed by using the P90 reserves value is vital in management of reserves risk.