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Various physico-chemical processes are affecting Alkali Polymer (AP) Flooding. Core floods can be performed to determine ranges for the parameters used in numerical models describing these processes. Because the parameters are uncertain, prior parameter ranges are introduced and the data is conditioned to observed data. It is challenging to determine posterior distributions of the various parameters as they need to be consistent with the different sets of data that are observed (e.g. pressures, oil and water production, chemical concentration at the outlet).
Here, we are applying Machine Learning in a Bayesian Framework to condition parameter ranges to a multitude of observed data.
To generate the response of the parameters, we used a numerical model and applied Latin Hypercube Sampling (2000 simulation runs) from the prior parameter ranges.
To ensure that sufficient parameter combinations of the model comply with various observed data, Machine Learning can be applied. After defining multiple Objective Functions (OF) covering the different observed data (here six different Objective Functions), we used the Random Forest algorithm to generate statistical models for each of the Objective Functions.
Next, parameter combinations which lead to results that are outside of the acceptance limit of the first Objective Function are rejected. Then, resampling is performed and the next Objective Function is applied until the last Objective Function is reached. To account for parameter interactions, the resulting parameter distributions are tested for the limits of all the Objective Functions.
The results show that posterior parameter distributions can be efficiently conditioned to the various sets of observed data. Insensitive parameter ranges are not modified as they are not influenced by the information from the observed data. This is crucial as insensitive parameters in history could become sensitive in the forecast if the production mechanism is changed.
The workflow introduced here can be applied for conditioning parameter ranges of field (re-)development projects to various observed data as well.
Hydrocarbon field (re-)development projects require the evaluation of a large number of development options under uncertainty. Furthermore, information of data gathering programs might result in narrowing parameter ranges and change the choice of the preferred development option.
The large number of development options (and decisions accordingly) which have to be taken under uncertainty leads to the necessity to determine the impact of the decisions on the (re-)development project objective. Knowing the sensitivity of the decisions on the project objective (e.g. NPV) allows for resource and data acquisition planning. The impact of decisions on the project value can be determined by performing a Generalized Sensitivity Analysis. This analysis does not replace Value of Information but facilitates planning and allows focusing on important decisions.
To further improve Decision Analysis and focus on important parameters, a Generalized Sensitivity Analysis of uncertain parameters on decisions can be performed. The advantage of such an investigation over sensitivity analysis on Oil Originally in Place (OOIP) or Net Present Value (NPV) is that it includes parameter interactions. Furthermore, it covers the impact of a parameter on the decisions directly rather than indirectly when OOIP or NPV sensitivity is used.
The analysis is shown at an example project in a Decision Analysis framework. The use of decision impact evaluation and parameter assessment on decisions might lead to more focused and faster hydrocarbon field (re-)development project execution.
Oil price forecasting has been shown to be challenging if not impossible for the long-term. However, the oil price has a major impact on Exploration and Production projects.
Historical Project Realized Oil Price (PROP) can be calculated for example projects by summing up the total project revenue using the actual oil prices and dividing through the total amount of oil produced. For different starting dates of example projects, the PROP changes. Determining the PROP for different starting times, a Cumulative Distribution Function (CDF) can be derived. Adjusting this CDF for expected "half cycle breakeven costs" for the low limit and demand considerations for the high case leads to a PROP range that can be used for future project evaluation.
Including PROP ranges into project evaluation allows for the selection of the most attractive development option, Value of Information analysis and project Probability of Economic Success (PES) calculation including oil price uncertainty.
Furthermore, using PROP ranges rather than oil price scenarios enables a distinction between short-term budget planning and long-term project development. For budget planning, a scenario approach is suggested while for long-term planning PROP ranges should be used. Applying long-term planning on PROP ranges leads to less fluctuation in staff planning and small annual adjustments in PROP range forecasting. Also, using PROP ranges results in increasing PES project hurdles at low oil prices and lower PES hurdles at high oil prices. Hence, at low oil prices the risk averseness of the company is increased. Another effect of using PROP ranges is that at high oil prices robustness of projects to low oil prices is included in the assessment.
To investigate the effect of PROP ranges on portfolio PES hurdles and project PES hurdles, a simplified linear-fit-model was developed. The results of the model showed that the project PES hurdles in a Value at Risk assessment can be determined applying the linear-fit-model to quantify the oil price dependency. The required individual project PES hurdles can be adjusted using the linear-fit-model to account for oil price uncertainty.
Steineder, Dominik (OMV Exploration and Production) | Clemens, Torsten (OMV Exploration and Production) | Osivandi, Keyvan (OMV Exploration and Production) | Thiele, Marco R. (Streamsim Technology and Stanford University)
Polymer injection might lead to incremental oil recovery and increase the value of an asset. Several steps must be taken to mature a polymer-injection project. The field needs to be screened for applicability of polymer injection, laboratory experiments have to be performed, and a pilot project might be required before field implementation.
The decision to perform a pilot project can be dependent on a value-of-information (VOI) calculation. The VOI can be derived by performing a work flow that captures the effects of the range of geological scenarios, as well as dynamic and polymer parameters, on incremental net present value (NPV). The result of the work flow is a cumulative distribution function (CDF) of NPV linked to prior distributions of model parameters and potential observables from the polymer-injection pilot.
The effect of various parameters on the CDF of the fieldwide NPV can be analyzed and in turn used to decide which measurements from the pilot have a strong sensitivity on the NPV CDF, and are thus informative. In the case shown here, the water-cut reduction in the pilot area has a strong effect on the NPV CDF of the polymer-injection field implementation. To extract maximum information, the response of the pilot for water-cut reduction needs to be optimized under uncertainty.
To calculate the VOI, the expected-monetary-value (EMV) difference of a decision tree with and without the pilot can be used if the decision maker (DM) is risk neutral. However, if the DM requires hurdle values through a probability of economic success (PES), value functions (VFs) and decision weights according to the prospect theory should be used. Applying risk hurdles requires a consistent use of VFs and decision weights for calculating VOI and the probability of maturation (POM) of projects.
The methodology was applied to assess the VOI for a horizontal-well pilot in the ninth Tortonian Horizon (9TH) Reservoir in Austria for a risk-averse DM. The operating parameters (polymer concentration and water injection) were chosen such that the watercut reduction, which was the most influential parameter of the polymer pilot on the field NPV CDF, was maximized.
Polymer injection might lead to incremental oil recovery and increase the value of an asset. Several steps have to be taken to mature a polymer injection project. The field needs to be screened for applicability of polymer injection, laboratory experiments have to be performed, and a pilot project might be required prior to field implementation.
The decision to perform a pilot project can be based on a Value of Information (VoI) calculation. The VoI can be derived by performing a workflow capturing the impact of the range of geological scenarios as well as dynamic and polymer parameters on incremental Net Present Value (NPV). The result of the workflow is a Cumulative Distribution Function (CDF) of NPV linked to prior distributions of model parameters and potential observables from the polymer injection pilot.
The impact of various parameters on the CDF of the field-wide NPV can be analyzed and in turn used to decide on what measurements from the pilot have a strong sensitivity on the NPV CDF and are thus informative. In the case shown here, the water cut reduction in the pilot area has a strong impact on the NPV CDF of the polymer injection field implementation. To extract maximum information, the response of the pilot for water cut reduction needs to be optimized under uncertainty.
To calculate the VoI, the Expected Monetary Value (EMV) difference of a decision tree with and without the pilot can be used if the Decision Maker (DM) is risk neutral. However, if the DM requires hurdle values through a Probability of Economic Success (PES), Value Functions (VF) and Decision Weights according to the Prospect Theory should be used. Applying risk hurdles requires a consistent use of VFs and Decision Weights for calculating VoI and the Probability of Maturation (POM) of projects.