Quantifying the Importance of Decisions and the Impact of Data Acquisition on Decisions in Hydrocarbon Field Re-Developments

Steineder, Dominik; Clemens, Torsten

doi:10.2118/200623-ms

Cited by 7 publications

References 29 publications

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Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

Clemens

Viechtbauer-Gruber

2020

Day 1 Tue, December 01, 2020

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Digitalization and Artificial Intelligence have impacted the oil and gas industry. Drilling of wells, predictive maintenance and digital fields are examples for the use of these technologies. In hydrocarbon production forecasting, numerical reservoir models and "digital twins" of reservoirs have been used for decades. However, increasing computing power and Artificial Intelligence recently enabled oil and gas companies to generate "digital siblings" of reservoirs (model ensembles) covering the uncertainty range in static data (e.g. petrophysics, geological structure), dynamic data (e.g. oil or gas properties) and economics (Capital Expenditures, Operating Expenditures). Machine Learning and Artificial Intelligence are applied to condition the model ensembles to measured data and improve hydrocarbon production forecasting under uncertainty. The model ensembles can be used for quantitative decision making under uncertainty. This allows companies to shorten the time for field (re-)development planning and to develop into learning organizations for decision making. These developments require companies to change the way of working in hydrocarbon production forecasting and decision analysis. Additional skills need to be developed in companies to embrace digitalization. Data science - which is considered a key skill in digitalization - has not been identified as crucial in skills development of oil and gas companies in the past. However, for data driven decision making, advanced data analytics skills and data science skills are a pre-requisite. To overcome this skill gap, staff needs to be trained and graduates with data science and profound physical and chemical skills need to be hired. Furthermore, skills development has to address the challenge of incorrect use of Machine Learning technologies and the risks of Artificial Intelligence leading to erroneous optimizations. In particular interpretability of AI needs to be covered in skills development.

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Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

Clemens

Viechtbauer-Gruber

2020

Day 1 Tue, December 01, 2020

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Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

Clemens

Viechtbauer-Gruber

2020

SPE Reservoir Evaluation &Amp; Engineering

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Summary Hydrocarbon (re-)development projects need to be evaluated under uncertainty. Forecasting oil and gas production needs to capture the ranges of the multitude of uncertain parameters and their impact on the forecast to maximize the value of the project for the company. Several authors showed, however, that the oil and gas industry has challenges in adequately assessing the distributions of hydrocarbon production forecasts. The methods for forecasting hydrocarbon production developed with digitalization from using analytical solutions to numerical models with an increasing number of gridblocks (“digital twins”) toward ensembles of models covering the uncertainty of the various parameters. Analytical solutions and single numerical models allow calculation of incremental production for a single case. However, neither the uncertainty of the forecasts nor the question in which the distribution of various outcomes the single model is located can be determined. Ensemble-based forecasts are able to address these questions, but they need to be able to cover a large number of uncertain parameters and the amount of data that is generated accordingly. Theory-guided data science (TGDS) approaches have recently been used to overcome these challenges. Such approaches make use of the scientific knowledge captured in numerical models to generate a sufficiently large data set to apply data science approaches. These approaches can be combined with economics to determine the desirability of a project for a company (expected utility). Quantitative decision analysis, including a value of information (VoI) calculation, can be done addressing the uncertainty range but also the risk hurdles as required by the decision-maker (DM). The next step is the development of learning agent systems (agent: autonomous, goal-directed entity that observes and acts upon an environment) that are able to cope with the large amount of data generated by sensors and to use them for conditioning models to data and use the data in decision analysis. Companies need to address the challenges of data democratization to integrate and use the available data, organizational agility, and the development of data science skills but making sure that the technical skills, which are required for the TGDS approach, are kept.

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Determining Initial Hydrocarbon Saturation Under Petrophysical Uncertainty Applying Machine Learning

Neff

Steineder

Stummer

et al. 2020

Day 1 Mon, November 09, 2020

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The initial hydrocarbon saturation has a major impact on field development planning and resource estimation. However, it is derived from indirect measurements from spatially distributed wells applying saturation height modelling based on uncertain parameters. The methodology presented here is deriving posterior parameter distributions by using Machine Learning in a Bayesian Framework honouring the petrophysical uncertainty in the field. The results are used for initialization and will be applied for forecasting under uncertainty. To determine the initial hydrocarbon saturation, the Saturation Height Model (SHM) needs to be conditioned to the interpreted logs. 2500 geological realizations were generated to cover the interpreted ranges of porosity, permeability and saturations for 15 wells. For the SHM, twelve parameters and their ranges were introduced. Latin Hypercube Sampling was utilized to generate a training set for Machine Learning models (ML). The trained ML models were conditioned to the log derived saturation data. To ensure a field wide consistency of the models only parameter combinations honouring the interpreted saturation range for all wells were selected. The presented method allows for consistent initialization and for rejection of parameters that do not fit to observed data. In our case study, the most significant observation concerns the posterior parameter distribution of the Free Water Level range which is narrowed down. Furthermore, the SHM parameters are proven independent; thus the resulting posterior parameter ranges for the SHM can be used for conditioning production data to models and subsequent hydrocarbon production forecasting. Additional observations can be made from the ML results such as the correlation between wells, this allows interpreting groups of wells that have a similar behaviour, favour the same combinations and potentially belong to the same compartment. A workflow is introduced honouring the interpreted error of log derived porosity-permeability and saturation data. The workflow is based on Saturation Height Modelling (SHM) to generate a training set for Machine Learning. The SHM is conditioned to all wells in a Bayesian Framework to ensure that the posterior parameter distributions of the SHM are consistent and can be used for conditioning dynamic models to dynamic data and production forecasting.

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Quantifying the Importance of Decisions and the Impact of Data Acquisition on Decisions in Hydrocarbon Field Re-Developments

Cited by 7 publications

References 29 publications

Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

Determining Initial Hydrocarbon Saturation Under Petrophysical Uncertainty Applying Machine Learning

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