We’re thrilled to introduce ALFAsim 2025.1. This new release now includes the complete Uncertainty and Risk Quantification framework, plus an updated corrosion plugin supporting both CO₂ and H₂S corrosion mechanisms. 

The Uncertainty Quantification (UQ) framework encompasses Sensitivity Analysis, History Matching, Uncertainty Analysis, and Risk/Cost Assessment tools designed to support better decision-making in flow assurance. This framework  quantifies uncertainties and evaluates risks during both the design and operational phases of a project.

Within this framework, Sensitivity Analysis helps identify which input parameters have the greatest impact on simulation results, presenting intuitive visual outputs such as tornado plots and bar charts. For projects with existing field data, Assisted History Matching allows you to calibrate models using either deterministic or Bayesian approaches, refining parameter uncertainties based on real production data for more reliable predictions.

Uncertainty Analysis supports both early-stage design and ongoing monitoring by allowing you  to simulate a range of possible outcomes based on estimated or observed data. With that, you can assess potential variations and their impact, even in the absence of field data. Finally, Risk/Cost Assessment combines event probability with severity and cost, providing a clear, quantifiable picture of project risks. This makes the Uncertainty and Risk Quantification framework a complete and powerful solution for minimizing uncertainty and maximizing confidence in flow assurance decisions.

The new Client-Server feature improves computational efficiency for demanding UQ simulations. Recognizing the high computational requirements of UQ frameworks, this release enables you to run simulations remotely on dedicated server infrastructure. By leveraging centralized processing power, this feature significantly accelerates simulation speeds, reduces local hardware constraints, and optimizes resource utilization.

The updated corrosion plugin enhances flow assurance capabilities by simulating the effects of CO₂ and H₂S corrosion, either individually or combined. This allows engineers to assess material degradation risks under varying operational conditions and select more suitable materials and corrosion mitigation strategies. Incorporating corrosion modeling into flow assurance workflows increases system reliability, reduces unplanned downtime, and supports more cost-effective asset integrity management throughout the lifecycle of the field.

With these advancements, ALFAsim 2025.1 marks a new stage in its evolution, introducing a unique Uncertainty and Risk Quantification architecture that shifts flow assurance simulations beyond deterministic results, enabling deeper insight and more robust decision-making under real uncertainties.

Major highlights of ALFAsim version 2025.1

Sensitivity Analysis

Local Sensitivity

• Consists on a set of simulations with specific variations (positive and/or negative values) in input parameters.
• The result is a tornado chart that shows the relative influence of each input variable.

Figure 1

Global Sensitivity

• Uses a stochastic algorithm based on the Morris Method (1991).
• Offers a random generation of simulations within a parameter space (value intervals) defined by you.
• Quantitative sensitivity indices are calculated for each input variable based on predefined output variables, each index reflects how much an input variable influences a specific output variable.
• The method used is also known as the Elementary Effects Method, since indices are calculated based on small (stepwise) changes in input variables.

Figure 2

History Matching

• Allows adjustment of input variables based on real historical field data.
• Improves the accuracy of projections for the studied case.
• Deterministic History Matching
  • Uses the SLSQP (Sequential Least Squares Programming) method based on minimization/optimization.
  • Ideal for preliminary analyses or cases with limited historical data.
  • Uses an objective function to compare simulated and historical data.
  • The optimal value is the one that minimizes the objective function (ideally close to zero).

Figure 3

Probabilistic (Bayesian) History Matching

• Requires a large number of simulations and a wide range of input parameter values.
• Uses Bayes’ Theorem for conditional probabilities and estimates the probability that a simulation result is close to historical data.
• Delivers a posterior probability distribution for each input variable.
• Provides a richer and more statistically robust analysis of model uncertainties.

Figure 4

Uncertainty Analysis

• Uses History Matching results to evaluate how uncertainties propagate through the numerical model.
• Provides a credibility interval output indicating where the correct solution of the case is likely to be located.
• Propagation
  • Works independently and propagates input uncertainties without using History Matching.
  • Reflects uncertainties associated with field measurement data.
  • Creates as outputs a credibility interval observed in the output variables.

Figure 5

Quantification

• Uses probability distributions adjusted based on historical field data, requires the History Matching process to be executed beforehand to obtain adjusted distributions.
• Provides better localization of where the true solution is likely to fall on the output graph.
• The historical data directly influences the final uncertainty quantification.

Figure 6

Risk/Cost Assessment

• Includes user-defined Impact Scale, Probability Levels, Risk Levels, Events, and Costs.
• Incorporates the uncertainties in the risk analysis, where the risks are weighted based on the severity levels.
• Allows you to define risk events, objectively identify which events have the highest likelihood of occurring, and evaluate the costs involved in each event.

Figure 7

CO₂ and H₂S Corrosion Plugin

• Now your flow assurance studies can be performed using the new corrosion plugin, supporting CO₂ and H₂S, individually or combined.
• You can simulate the corrosion rate of CO₂ (sweet corrosion) and H₂S (sour corrosion) using default or custom water compositions.
• You can choose from several corrosion frameworks: CO₂ corrosion, H₂S corrosion, CO₂ and H₂S corrosion considering the maximum separated corrosion rate, CO₂ and H₂S  considering the sum of the separated corrosion rates, or CO₂ and H₂S using the Sun&Nesic model.

Figure 8

Figure 9

Improvements

• New Error Report Workflow: Now, the reported errors are converted into support tickets, streamlining communication and ensuring that issues are properly tracked, prioritized, and addressed by the support team. This optimization improves response time and helps you focus resources on the most critical problems.

• ALFAsim Web Manual Integration: You can now easily access relevant documentation and guidance, as all user manuals and technical materials are centralized in one place.