Understanding the integrated performance of complex artificially lifted wells on not normally manned (NNM), offshore platforms without invasive techniques represents a challenge not only to minimizing operating costs but also to optimizing production and thereby maximizing value. Often the analysis of such problems is hindered by the complex interactions between identified production constraints and by a lack of operating data.
The Cliff Head oil field (offshore Western Australia) is developed with an innovative coiled-tubing deployed-electrical-submersible-pump (CT-ESP) artificial-lift system. This paper describes the process by which ESP and well data, in conjunction with a well-performance-modeling software, have been used as a powerful tool to diagnose well-performance issues and optimize production. Production trends were created on the basis of real-time production data to understand ESP performance. Individual-well models were created to identify potential causes of declining performance--in this case, the use of an ESP performance-limiting factor (PLF) indicating deteriorating ESP performance because of solids buildup.
On the basis of the model results, chemical soaks were implemented on two production wells to remove flow restrictions within and around the ESPs. The treatments increased the oil-production rates by 17 to 48%.
Following a debottlenecking study, reservoir simulation in combination with detailed ESP-performance analysis concluded that total-field-production improvements of up to 50% were possible. Consequently, the next phase of field development will install larger-capacity ESPs.
This paper outlines how field data and desktop tools were combined successfully to monitor and diagnose well-performance issues to deliver material production enhancements.
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.