Evolution of Uncertainty Methods in Decline Curve Analysis

Purvis, Dwayne Christopher; Kuzma, Heidi Anderson

doi:10.2118/179980-ms

Cited by 5 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, uncertainty-quantification algorithms have been widely applied to decline-curve analysis (Cronquist 1991;Chang and Lin 1999;Cheng et al 2008;Gong et al 2014;Fulford et al 2016;Yu et al 2016). Purvis and Kuzma (2016) provides an overview of methods commonly used. However, the pure application of such algorithms still can result in biased estimates and frequently in overconfidence.…”

Section: Introductionmentioning

confidence: 99%

Combining Physics, Statistics, and Heuristics in the Decline-Curve Analysis of Large Data Sets In Unconventional Reservoirs

Holanda

Gildin

2018

SPE Reservoir Evaluation &Amp; Engineering

View full text Add to dashboard Cite

Analytical single-well models have been particularly useful in forecasting production rates and estimated ultimate recovery (EUR) for the massive number of wells in unconventional reservoirs. In this work, a physics-based decline-curve model accounting for linear flow and material balance in horizontal multistage-hydraulically-fractured wells is introduced. The main characteristics of pressure diffusion in the porous media and the fact that the reservoir is a limited resource are embedded in the functional form, such that there is a transition from transient to boundary-dominated flow and the EUR is always finite. Analogously to the frequently used Arps (1945) hyperbolic model, the new model has only three parameters, where two of them define the decline profile and the third one is a multiplier. This model is applied to a large data set in a work flow that incorporates heuristic knowledge into the history matching and uncertainty quantification by assigning weights to rate measurements. The heuristic rules aim to lessen the effects of nonreservoir-related variations in the production data (e.g., temporary shut-in caused by fracturing in a neighboring well) and emphasize the reservoir dynamics to perform reliable predictions. However, there are additional degrees of freedom in the way these rules define the values of the weights; therefore, a criterion is established that "calibrates" the uncertainty in the probabilistic models by adjusting the parameters in the heuristic rules. Uncertainty quantification and calibration are performed using a Bayesian approach with hindcasts. This methodology is implemented in an automated framework and applied to 992 gas wells from the Barnett Shale. A comparison with the Arps (1945) hyperbolic model, the Duong (2011) model, and stretched exponential model for this data set shows that the new model is the most conservative in terms of estimated reserves.

show abstract

Section: Introductionmentioning

confidence: 99%

Combining Physics, Statistics, and Heuristics in the Decline-Curve Analysis of Large Data Sets In Unconventional Reservoirs

Holanda

Gildin

2018

SPE Reservoir Evaluation &Amp; Engineering

View full text Add to dashboard Cite

show abstract

Combining Physics, Statistics and Heuristics in the Decline Curve Analysis of Large Datasets in Unconventional Reservoirs

Holanda

Gildin

2017

SPE Latin America and Caribbean Petroleum Engineering Conference

View full text Add to dashboard Cite

Analytical single well models have been particularly useful in forecasting production rates and Estimated Ultimate Recovery (EUR) to the massive number of wells in unconventional reservoirs. In this work, a physics-based decline curve model accounting for linear flow and material balance in horizontal multi-stage hydraulically fractured wells is introduced. The main characteristics of pressure diffusion in the porous media and the fact that the reservoir is a limited resource are embedded in the functional form, such that there is a transition from transient to boundary dominated flow and the EUR is always finite. Analogously to the frequently used Arps hyperbolic, the new model has only three parameters, where two of them define the decline profile and the third one is a multiplier. This model is applied to a large dataset in a workflow that incorporates heuristic knowledge into the history matching and uncertainty quantification by assigning weights to rate measurements. The heuristic rules aim to lessen the effects of non-reservoir related variations in the production data (e.g. temporary shut-in due to fracturing in a neighboring well) and emphasize the reservoir dynamics to perform reliable predictions. However, there are additional degrees of freedom in the way these rules define the values of the weights, therefore a criteria is established that "calibrates" the uncertainty in the probabilistic models by adjusting the parameters in the heuristic rules. Uncertainty quantification and calibration is performed via a Bayesian approach with hindcasts. This methodology is implemented in an automated framework and applied to 992 gas wells from the Barnett shale. A comparison with the Arps hyperbolic, Duong and stretched exponential models for this dataset shows that the new model is the most conservative in terms of estimated reserves.

show abstract

Uncertainty Assessment on Short-Term Production Forecasts Using Integrated Asset Modeling and Experimental Design

Purwar

Sousa

et al. 2017

Day 2 Mon, October 16, 2017

View full text Add to dashboard Cite

In field development, many decision-make processes are associated with uncertainties, which sometimes can expose the project to significant engineering and economic risks. Accurate uncertain assessment on field-scale production forecast requires multidisciplinary approach, such as Integrated Asset Modeling (IAM), to accommodate subsurface response, well performance, and surface facilities simultaneously. However, the added complexity can make IAM prohibitively and computationally expensive and time-consuming, particularly for uncertainty analysis and optimization studies, which normally demand thousands of simulation runs. This paper presents a workflow that effectively integrates all the production components to an IAM then generates its proxy model for faster deployment. First, to properly configure the model, some practical sensitivity analyses were performed on how reservoir model selection and forecast period affect the uncertainty assessments. Second, in order to generate the proxy model, two-level screening design was first used to reduce the number of variables. Third, simulations through experimental design were conducted. The resulting response surface was used to fit a multiple regression model to reduce simulation runtime and maintain prediction accuracy at the same time. The results suggested that in order to build accurate IAM and avoid misleading uncertainty results, it is recommended to take careful considerations to select proper tools to model different components of integrated production system. It was also found that the relative importance or influence of some parameters will not remain constant but evolve through the life of field. Thus, it is necessary to keep the production forecast period of uncertainty analysis consistent with the development period associated with field strategies and economic objectives. To reduce the computational cost and accelerate decision-making process, a proxy model was generated to approximate this physical IAM model with high correlation coefficient of 99.65%. In case study, the high impact factors that can explain most variations in response were identified from totally 29 uncertain variables through screening design, including water-oil contact, thickness, oil density, initial reservoir pressure, and porosity. The validation results showed that proxy model predictions matched closely with simulation results from actual IAM with correlation coefficient of 95.70%. Based on a synthetic production forecast uncertainty study using Monte Carlos simulation, the proxy model can reduce the runtime for five-thousand simulations to seconds from hundreds of hours if using fully physical IAMs. Although using proxy model generated through experimental design for reservoir simulation studies is common approach, it has not been applied to integrated asset modeling which couples reservoir simulator with well and surface facilities. This workflow allows efficient and accurate uncertainty estimation for production forecast and field-scale optimizations.

show abstract

Evolution of Uncertainty Methods in Decline Curve Analysis

Cited by 5 publications

References 20 publications

Combining Physics, Statistics, and Heuristics in the Decline-Curve Analysis of Large Data Sets In Unconventional Reservoirs

Combining Physics, Statistics, and Heuristics in the Decline-Curve Analysis of Large Data Sets In Unconventional Reservoirs

Combining Physics, Statistics and Heuristics in the Decline Curve Analysis of Large Datasets in Unconventional Reservoirs

Uncertainty Assessment on Short-Term Production Forecasts Using Integrated Asset Modeling and Experimental Design

Contact Info

Product

Resources

About