CATEGORIZED | 2008, March/April

New tool for blowout risk management allows cross communication among decision-makers

Posted on 30 October 2009

By Øystein Arild, International Research Institute of Stavanger

A blowout is among the most severe events that can happen during exploitation of petroleum resources. Particularly in environmentally sensitive areas, where uncertainties about consequences are high and contingency planning for major accidents requires extra attention, oil spill preparedness is critical. In well planning on the Norwegian Continental Shelf, calculations of blowout flow and duration from potential blowouts are important parts of environmental risk management.

E&P company practices vary in their level of detail, assessment of uncertainty, nomenclature, documentation and traceability of the blowout analysis. Due to this lack of international and national standards for environmental risk analysis, the Norwegian Oil Industry Association (OLF) has developed guidelines to standardise nomenclature, procedure and documentation of blowout calculations.

BlowFlow, developed by the International Research Institute of Stavanger (IRIS), is a software tool developed for evaluation of blowout scenarios by enabling a risk-based quantification of blowout flow duration, volumes and rates for input to environmental risk analysis. It is a multidisciplinary tool that allows cross communication among geologists, drilling engineers and HSE engineers and builds on the OLF guidelines.

Blowout risk management

Figure 1 shows a typical blowout risk management chain; BlowFlow addresses step 2 in the chain. Calculation of blowout rate and duration is the basis for oil spill drift forecasts, looking at the amount of oil that will be present in an environmental habitat. Based on the amount of oil in the habitat, the impact on the wildlife, in terms of fatalities, and finally the restitution time for the habitat is taken into consideration. Because many blowout scenarios are possible, the uncertainties in the blowout scenarios should ideally be propagated through the “environmental impact chain.”

The creation of blowout scenarios as shown in the first box in Figure 1 may be performed in two ways. First, a more detailed study based on a relevant study of kick and loss of well control scenarios, as illustrated by means of the first two boxes in Figure 2. Or, second, create the blowout scenarios directly based on expert input and discussion. The latter is typically the case if there is not enough time and resources to perform the full pre-analysis as shown in Figure 2.

Decisions related to blowout risk

Having seen how the blowout rate and duration analysis enter into the larger picture of environmental impact analysis, the main reason for performing such a study is to support decisions and to be able to direct efforts related to risk management. We will first discuss the decisions that typically are supported and secondly the risk reduction process.

The decisions to be supported based on the output from a blowout rate and duration analysis are:

• Acceptance of the risk level related to a planned stand-alone drilling operation, for example, an exploration or appraisal well.

• Acceptance of the risk level related to a planned installation, for example, a new installation, a modification of an existing installation or the activity level.

• Acceptance of the risk level of a planned activity that requires evaluation on a field level, for example, a new field or a considerable extension of current activities.

• Dimensioning of oil spill preparedness, which includes everything from large offshore skimming systems and vessels to bird rehabilitation kits.

• Relief well design.

With respect to risk reduction, the purpose of the exercise is to:

• Identify risk-reducing measures.

• Use the results in an ALARP (“As Low As Reasonably Practicable”) setting by working further with risk-reducing measures until the risk level is as low as possible under the given financial and technical constraints. An example of a risk-reducing measure following a BlowFlow study could be using smaller hole size in the reservoir section.

Today’s and past blowout risk analyses

As of today, there is no common standard or methodology among E&P companies for the calculation of blowout rate and duration. Methods used vary from company to company, and the reasoning behind the selected methodology is hard to access, with parts of the calculations hard to track. In addition, the interpretation of central measures used in the calculation may be vague or diffuse. The result is that one cannot compare calculated rates from field to field and from company to company. It’s unclear which approach gives the best prediction, whether uncertainty is handled properly and how applicable one methodology is to a given situation.

To exemplify the plethora of methods that are being used or have been used, two examples will be given. They may represent extremes but are used here to illustrate the point that different approaches are being or have been pursued. For simplicity, we assume that an estimation of blowout volume is to be performed.

A purely statistical approach uses worldwide blowout statistics to estimate the potential blowout volume related to a drilling operation. Such a statistical-based tool seeks to familiarize any future drilling operation to one that has occurred in the past, and calculates the blowout volume based on historical data. The major disadvantage of this method is that the uniqueness of a drilling operation is hard to incorporate into the analysis. However, it is relatively easy to communicate.

Another approach is the very conservative approach, where one or a few conservative scenarios are selected, then the worst-case blowout volumes are calculated based on this. The major drawback of this method is that the scenarios generated may be unrealistic, thus over-dimensioning risk-reducing measures that must be taken based on such scenarios.

Neither of these methodologies provide the framework needed for making sound risk management decisions.

Analysis tool – principles

At the outset of the BlowFlow development project, the following properties were desired for the software tool:

• It provides a common platform for communication between HSE engineers and drilling engineers.

• The results from the analysis can be used as input to a total environmental impact analysis.

• The results from the analysis are easy to communicate to all involved parties.

• The results from the analysis are transparent and provide guidance with respect to which factors that are most important.

Based on the above goals, the main principles of the methodology were chosen to be:

• Use a range of defined blowout scenarios as a basis for the analysis.

• Describe uncertainty related to the sequence of events following a blowout.

• Describe uncertainty related to the blowout rate and a cumulative discharged volume by means of probability distributions.

• Describe uncertainty related to blowout duration by means of a probability distribution.

The work process related to using the tool consists of three phases: input, calculation and output (Figure 3). Details of each phase are explained below.

The first phase is input, where parameters such as well geometry, subsurface parameters, possible flow paths in the well and the exit points where a blowout may leave the well into the surroundings, are specified. In addition, blowout-stopping mechanisms that give the basis for judging the blowout duration are assessed. The pre-defined stopping mechanisms covered in the tool are capping, coning, bridging, relief well and natural cessation, but other stopping mechanisms may be defined by the user.

In terms of assessing the input parameters, the well design parameters can be taken from the drilling program, while parameters that are uncertain, such as reservoir pressure, must be assessed by relevant experts. The assessment of uncertain parameters are done by means of probability distributions — an example is provided in Figure 4. This allows the experts to express uncertainty on a relatively detailed level, thereby easing the probability assessment process.

Moreover, the detailed level of input assessment provides transparency, both in terms of what people were involved and the impact of a “low level” parameter on the end result of the analysis.

Assessment of input parameters is the most time-consuming part of a BlowFlow analysis. After assessing these, the user can proceed to phase two — calculation. This simply consists of clicking the “run” button in the BlowFlow tool, which will then simulate a pre-defined number of blowout scenarios, say 5,000, and records what happened in each scenario. In a technical language, a large number of simulations of possible future scenarios is called the Monte Carlo simulation.

Together with a very fast steady-state solver for the calculation of blowout flow rates, a Monte Carlo simulator is the underlying engine for propagating uncertainty in input parameters to the end results in BlowFlow.

The output phase shows a summary of what happened in each of the 5,000 blowouts that were simulated. This is expressed through probability distributions for total discharged hydrocarbon volume, blowout duration and blowout rates. In addition to this, sensitivity analysis allows the analyst to gain insight into which input parameters contributed the most to the results. With these results at hand, the analyst has a good starting point for communicating the results to the involved parties.

Example – North Sea Well

We illustrate the examples and theory as described above by showing an example from a North Sea well. Assume we want to drill a specified production well from a floating rig  and will use the methodology described above to provide a blowout risk analysis. We will go through the phases described in the previous section:

1) Assess the input parameters.

2) Run the analysis.

3) Present the results.

In this section, we will specify some of the details of each the work process phases. Some of the drilling and subsurface parameters that were used in the example are shown in Table 1.

Further, a set of scenarios were developed based on the input. Note that, in reality, there are an infinite number of scenarios. However, we have chosen to show a discrete set of scenarios (Figure 5) in order to illustrate how scenarios are generated.

With respect to stopping mechanisms, we considered two:

• The blowout stops due to coning.

• The blowout stops due to a successful dynamic killing by means of a relief well.

For the relief well, we took into consideration issues such as time to mobilize the rig, time to construct the relief well and time to kill the well. Other stopping mechanisms were not considered for simplicity but could easily be added.

With a description of the well and an understanding of the uncertainties in the subsurface parameters and stopping mechanisms, BlowFlow was now able to generate multiple blowout scenarios.

A variety of results can be taken from the simulations. Figures 6 to 8 present the main results — blowout rates versus time curves, the blowout duration probability distribution and the total discharged oil volume probability distribution. For each result under study, we performed a sensitivity study to obtain insight on what were the most important parameters.

Summarizing this example, it demonstrates that the principles of a BlowFlow study satisfy a sound blowout risk management process by:

• Supporting decisions through informing the decision-maker about potential outcomes and driving mechanisms for the range of outcomes.

• Directing efforts with respect to risk management — decide whether risks should be avoided, transferred, mitigated or accepted.

• Providing transparency of the analysis in order to facilitate expert discussions, peer reviews and for being pedagogical on how to perform probabilistic risk analysis.

• Provide a common platform for probabilistic blowout rate and duration analysis and improve communication between the involved parties.

About the author: Øystein Arild is research director of the risk management group of the International Research Institute of Stavanger.

Acknowledgements: The author would like to acknowledge the valuable comments given by Thomas Nilsen, StatoilHydro. He would also like to thank StatoilHydro and Eni for sponsoring the project.

This article is based on a presentation at the IADC Well Control Europe Conference & Exhibition, 9-10 April 2008, Amsterdam.

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