Yueming Cheng

Quantification of Uncertainty in Reserve Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs

Decline curve analysis is the most commonly used technique to estimate reserves from historical production data for the evaluation of unconventional resources. Quantifying the uncertainty of reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in the analysis of unconventional oil or gas wells. Probabilistic approaches are sometimes used to provide a distribution of reserve estimates with three confidence levels (P10, P50, and P90) and a corresponding 80% confidence interval to quantify uncertainties. Our investigation indicates that uncertainty is commonly underestimated in practice when using traditional statistical analyses. The challenge in probabilistic reserve estimation is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. In this paper, we present an advanced technique for the probabilistic quantification of reserve estimates using decline curve analysis. We examine the reliability of the uncertainty quantification of reserve estimates by analyzing actual oil and gas wells that have produced to near-abandonment conditions, and also show how uncertainty in reserve estimates changes with time as more data become available. We demonstrate that our method provides a more reliable probabilistic reserve estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.

A Practical Nonlinear Regression Technique for Horizontal Well Test Interpretation

Abstract A practical nonlinear regression technique for analysis of horizontal well pressure transient tests is presented. This technique can provide accurate and reliable estimation of well-reservoir parameters if downhole flow rate data are available. In situations without flow rate measurement, reasonably reliable parameter estimation can be achieved by using the detected flow rate from blind deconvolution. This technique has the advantages of eliminating the need for estimation of the wellbore storage coefficient and providing reasonable estimates of effective wellbore length. It provides a practical tool for enhancement of horizontal well test interpretation, and its practical applicability is illustrated by synthetic and actual field cases.

Quantification of Uncertainty in Reserves Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs

Decline curve analysis is the most commonly used technique to estimate reserves from historical production data for evaluation of unconventional resources. Quantifying uncertainty of reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in analysis of unconventional oil or gas wells. Probabilistic approaches are sometimes used to provide a distribution of reserve estimates with three confidence levels (P10, P50 and P90) and a corresponding 80% confidence interval to quantify uncertainties. Our investigation indicates that uncertainty is commonly underestimated in practice when using traditional statistical analyses. The challenge in probabilistic reserves estimation is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. In this paper, we present an advanced technique for probabilistic quantification of reserve estimates using decline curve analysis. We examine the reliability of uncertainty quantification of reserve estimates by analyzing actual oil and gas wells that have produced to near-abandonment conditions, and also show how uncertainty in reserves estimates changes with time as more data become available. We demonstrate that our method provides more reliable probabilistic reserves estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.Copyright

Advanced Deconvolution Technique for Analyzing Multirate Well Test Data

Deconvolution allows the test analyst to estimate the constant-rate transient pressure response of a reservoir-well system, and assists us in system identification and parameter estimation. Unfortunately, deconvolution amplifies the noise contained in data. Often, we cannot identify the reservoir system from deconvolved results owing to solution instability caused by noise in measured data. We previously presented a deconvolution technique based on the fast Fourier transform that we applied to a single buildup or drawdown period. In this paper, we extend our previous work and apply the deconvolution technique based on the fast Fourier transform to arbitrarily changing rate profiles such as multirate tests. The deconvolution results, which represent a constant-rate pressure drawdown response spanning the entire duration of the test, can provide helpful insight into the correct reservoir description. We have improved our original deconvolution method in number of ways, particularly with the introduction of an iterative algorithm that produces stable deconvolution results. We demonstrate application of our deconvolution method to analysis of synthetic and field examples, including both flow and shut-in periods. Our deconvolution method can efficiently reproduce the characteristic responses of the reservoir-well system and increase our confidence in parameter estimates.