Integrating Model Uncertainty in Probabilistic Decline-Curve Analysis for Unconventional-Oil-Production Forecasting

Abstract

Summary Decline-curve analysis (DCA) for unconventional plays requires a model that can capture the characteristics of different flow regimes. Thus, various models have been proposed. Traditionally, in probabilistic DCA, an analyst chooses a single model that is believed to best fit the data. However, several models might fit the data almost equally well, and the one that best fits the data might not best represent the flow characteristics. Therefore, uncertainty remains regarding which is the “best” model. This work aims to integrate model uncertainty in probabilistic DCA for unconventional plays. Instead of identifying a single “best” model, we propose to regard any model as potentially good, with goodness characterized by a probability. The probability of a model being good is interpreted as a measure of the relative truthfulness of this model compared with the other models. This probability is subsequently used to weight the model forecast. Bayes' law is used to assess the model probabilities for given data. Multiple samples of the model-parameter values are obtained using maximum likelihood estimation (MLE) with Monte Carlo simulation. Thus, the unique probabilistic forecasts of each individual model are aggregated into a single probabilistic forecast, which incorporates model uncertainty along with the intrinsic uncertainty (i.e., the measurement errors) in the given data. We demonstrate and conclude that using the proposed approach can mitigate over/underestimates resulting from using a single decline-curve model for forecasting. The proposed approach performs well in propagating model uncertainty to uncertainty in production forecasting; that is, we determine a forecast that represents uncertainty given multiple possible models conditioned to the data. The field data show that no one model is the most probable to be good for all wells. The novelties of this work are that probability is used to describe the goodness of a model; a Bayesian approach is used to integrate the model uncertainty in probabilistic DCA; the approach is applied to actual field data to identify the most-probable model given the data; and we demonstrate the value of using this approach to consider multiple models in probabilistic DCA for unconventional plays.

Removing the Outlier from the Production Data for the Decline Curve Analysis of Shale Gas Reservoirs: A Comparative Study Using Machine Learning

“…Hong et al indicated that the least-squares estimation is a special case of MLE but a more-accurate data point will have more weight than a less-accurate data point using MLE, which improves the goodness of fitting. 21 Size of the historical data: the less the production data size, the higher the uncertainty. Production data quality: due to changes in the operating conditions with time to control BHP and multiple shut-ins, noise, outliers, and fluctuations of the historical production data appear and affect the fitting and therefore the reliability of the forecasting.…”

“…In DCA, OLS is usually optimistic, while WLS is conservative, as shown in Figure . Hong et al indicated that the least-squares estimation is a special case of MLE but a more-accurate data point will have more weight than a less-accurate data point using MLE, which improves the goodness of fitting Size of the historical data: the less the production data size, the higher the uncertainty. Production data quality: due to changes in the operating conditions with time to control BHP and multiple shut-ins, noise, outliers, and fluctuations of the historical production data appear and affect the fitting and therefore the reliability of the forecasting. Flow regime variations through the production life: different transient flow regimes appear and could last for a very long time. There are many steps to decrease the degree of uncertainty related to DCA.…”

“…In DCA, OLS is usually optimistic, while WLS is conservative, as shown in Figure . Hong et al indicated that the least-squares estimation is a special case of MLE but a more-accurate data point will have more weight than a less-accurate data point using MLE, which improves the goodness of fitting …”

Machine learning based decline curve analysis for short-term oil production forecast

“…(Gonzalez et al, 2012)) have attempted to identify a single ed as Pan CRM is this paper. Some researchers (e. Hong et al, 2019) have argued that selecting a single ed as Pan CRM is this paper. Some researchers (e.g.…”

“…Gonzalez et al, 2012) have attempted to identify a single "best" model among several DCA models. However, Hong et al (2019) have argued that selecting a single "best" model eliminates other potentially good models and exhibits overconfidence (i.e., trust the single model 100%), which can cause significant over/underestimates. Thus, their proposed approach incorporates multiple models by using Monte Carlo simulation to assess the probability of each model and consequently provides a probabilistic forecast of production.…”