Probabilistic planning offers well-informed insight into most optimum : AGR

Probabilistic planning approach goes far beyond the often limited and unreliable conventional risk screening methods. Following a rigorous analysis of all risks and thousands of possible outcomes that probabilistic planning offers, operators can make far better informed decisions to meet the most optimum time and cost overview

A two-step pragmatic probabilistic screening technique during the initial planning phase transforms the quality of information which is needed for preparing for intervention and abandonment campaigns. It results in a transparent, robust and repeatable risk based approach. Probabilistic planning method also generates evidence for risk identification, and mitigation. This information is valuable in support of regulatory approvals.

How does it work?

The first step is a conventional screening process where all possible options are highlighted and a high level sequence of possible events is generated.

Options carrying unacceptable risks are removed. For those that remain, the probabilistic modelling kicks in, offering in-depth, risk based profiles using available data and relevant case experiences.

Outputs are compared, risk assessed and peer reviewed to select a base case which can be taken forward into the detailed planning phase. Less favourable options removed in this process are clearly documented to avoid repetition of work.

Why prefer probabilistic modelling over deterministic?

Critical concerns

Probabilistic models

Deterministic models

Incorporating risks and
building in offset
experiences

Event models can be created to mimic decision/outcome trees
Risks can be added on both phase level and/or event level
Event level risks can be as simple or complicated as required
Distributions can be assigned to phase level risks (e.g. WOW allocation may be set between 2 and 10%, providing a more representative picture for P10 and P90 outcomes)
Event level risks can be assigned percentages based on offset experiences and incorporated into well targets

Single, all encompassing uplifts for phase based risks. For example, WOW and NPT
Limited event based uplift without the use of external decision/outcome trees

Sensitivity analysis

Ability to perform sensitivity analysis, taking into account contingency operations and range of well outcomes
Providing a comprehensive screening opportunity when evaluating high dollar contracts (e.g quantifying the value of a higher tripping speed of a modern, high value rig if multiple attempts to recover casing are required)

Deterministic assessment within a well set-up model allows for sensitivities based on a single, linear set of outcomes

Setting well targets

Outcomes consider both well conditions and performance
P10 outcomes incorporate technical limit performance and favourable well conditions
P90 outcomes reflect heavy contingency and poor performance
Visualising all well outcomes in distribution of scenarios means meaningful well targets can be set

Conventional technical limit and contingency outcomes from deterministic models are directly dependent on predicting well conditions and outcomes – a challenge for well abandonment

Operational uses

Ability to incorporate learnings and change risk percentage allocations as operations progress allows calibration, rescreening and re-categorisation of base case/opportunity cases – potentially saving days of rig time
Real-time data allows rapid decision making supported by numerical evidence

Tuning of uplifts during ops may allow for contingency budget allocations to be reconsidered
Learnings retained for use in future time and cost estimates

Probabilistic intervention and decommissioning approach maximises performance

We have a comprehensive database of abandonment model templates, based on our extensive global operational experience. It means our users receive an efficient start to creating their initial models and can move quickly into the detailed risk building process for probabilistic models.

iQx™ PLANS™ is a proven probabilistic time and cost estimation application built by well engineers, for well engineers. It employs Monte Carlo simulation, modelling thousands of probable well outcomes to produce an extensive risk assessment of key project aims.

This insight can be relied on during the detailed planning phase. It informs decisions and focus resources.

PLANS™ presents variety of opportunities

The advantages of using PLANS™ and probabilistic planning methodology are proving key to energy companies looking for increased agility and performance:

Transparent

All available options are considered and peer reviewed in a wider forum to offer alignment on key risks and model factors. It’s a process that documents risk identification to support regulatory approvals.

Robust

This approach brings an all encompassing, pragmatic approach to model development. It’s a process that is documented and justified.

Scalable

It offers the flexibility to create simple and high level models or complex, multi-branched examples that need additional focus. Models are also scalable to well, phase, event or entire campaign.

Repeatable

Thousands of simulations are run, giving a repeatable distribution of all probable well outcomes. Models can be easily tweaked for similar wellbores, saving significant time and effort for field abandonments.

Unbiased

This method quantifies risks and compares outcomes from different methodologies on a like-for-like basis. It offers an unbiased approach, unaffected by individual bias or previous experiences.

Prompting

It prompts the engineer to always consider the next ‘what-if’, allowing them to visualise the effect on well outcomes.

Quantitative

Quantitative outputs show a range of possible timings based on probable well outcomes. Risk uplifts are quantitative and based on experience and offset data rather than an all prevailing NPT uplift.

To learn more about how AGR can support your requirements in probabilistic planning, please contact us.

Well Kill

Probabilistic planning offers well-informed insight into most optimum well intervention and abandonment methodology