No abstract

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.

OMV Austria E&P GmbH operates 26 oil fields in Lower Austria. The majority was developed in the 1950s and 1960s and shows an extended decline period. The challenge of operating brown fields is seen to maintain a reasonable oil production over time in a cost effective manner - this can be tackled by a major chemical enhanced oil recovery field redevelopment project. The scope of the polymer field rollout is to create and efficiently operate horizontal polymer injection patterns in two horizons in Lower Austria. OMV follows the strategy to pilot new reservoir and production technologies before their application in field rollouts. Due to the large project investment volume it is crucial to derive information and lessons learned from existing pilot patterns to optimize conceptual decisions on artificial lift, completion, sand control and injection strategy and to reduce technical risk. In the past 10 years several vertical pilot patterns were created to analyze the efficacy of polymer injection in the Tortonian Horizon. Significant operational experience in water treatment, polymer injection and polymer back production, but also in tracer testing was established. Since already the vertical polymer patterns showed not only operational success, but also significant incremental oil production, the idea of horizontal flooding patterns was born. To confirm and understand the impact of polymer injection also in horizontal wells, a first horizontal pilot pattern was drilled; additional pilot wells are currently following. For a future field redevelopment the technology selection should be de facto based on either standard or pilot-proven technologies. This paper describes measures undertaken in the discipline of production technology to prepare for one of the largest field redevelopment projects in the history of OMV Austria. These measures include the application of several pilot projects in the field of artificial lift, completion design and sand control, production and injection allocation. Therewith production technology serves the needs for active reservoir management and thus, follows a holistic field development approach.

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 are challenging to perform as a number of decisions need to be taken under uncertainty. In addition, data gathering activities need to be performed to decrease the risk of negative outcomes related to project objectives and to select the development option with the highest value. Many oil and gas E&P companies are using as stage-gate process to mature hydrocarbon field (re-)development projects. Such a process allows for systematic project development including value assurance measures such as peer reviews prior to decision gates. However, the stage-gate leads to challenges if it is not performed in a Bayesian framework. If for example simplified models are used in the early phase of the stage-gate process, no Bayesian updating of believes can be done. Similarly, if the company is not seamlessly integrating project value in project development, the value of development option and of data acquisition cannot be quantitatively determined. We are showing how to mature projects in a Bayesian framework. We show how data gathering is used to update believes and how generalized sensitivity analysis can be applied to identify which decisions have an impact of project value and which parameters are sensitive to the decision which development option to perform. The key performance indicators such as Expected Monetary Value, Probability of Maturation, Probability of Economic Success are updated. The changes of the sensitivities of the various uncertain parameters with project maturation is monitored and the decision maker is supplied with key performance indicators and residual risks, risk mitigation and management plans at Final Investment Decision.