An efficient automatic history matching method through the probabilistic collocation based particle filter for shale gas reservoir

This study developed a production-forecasting model to replace the numerical simulation and the decline curve analysis using reservoir and hydraulic fracture data in Montney shale gas reservoir, Canada. A shale-gas production curve can be generated if some of the decline parameters such as a peak rate, a decline rate, and a decline exponent are properly estimated based on reservoir and hydraulic fracturing parameters. The production-forecasting model was developed to estimate five decline parameters of a modified hyperbolic decline by using significant reservoir and hydraulic fracture parameters which are derived through the simulation experiments designed by design of experiments and statistical analysis: (1) initial peak rate ( P hyp ), (2) hyperbolic decline rate ( D hyp ), (3) hyperbolic decline exponent ( b hyp ), (4) transition time ( T transition ), and (5) exponential decline rate ( D exp ). Total eight reservoir and hydraulic fracture parameters were selected as significant parameters on five decline parameters from the results of multivariate analysis of variance among 11 reservoir and hydraulic fracture parameters. The models based on the significant parameters had high predicted R 2 values on the cumulative production. The validation results on the 1-, 5-, 10-, and 30-year cumulative production data obtained by the simulation showed a good agreement: R 2 > 0.89 . The developed production-forecasting model can be also applied for the history matching. The mean absolute percentage error on history matching was 5.28% and 6.23% for the forecasting model and numerical simulator, respectively. Therefore, the results from this study can be applied to substitute numerical simulations for the shale reservoirs which have similar properties with the Montney shale gas reservoir.

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History Matching with Ensemble Kalman Filter Using Fast Marching Method in Shale Gas Reservoir

10.2118/176164-ms ◽

2015 ◽

Cited By ~ 3

Author(s):

Jihun Leem ◽

Kyungbook Lee ◽

Joe M. Kang ◽

Yonjun Park ◽

Jihye Park

Keyword(s):

Kalman Filter ◽

Shale Gas ◽

Ensemble Kalman Filter ◽

History Matching ◽

Fast Marching Method ◽

Fast Marching ◽

Gas Reservoir ◽

Shale Gas Reservoir ◽

Marching Method

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A Novel Optimization Workflow Coupling Statistics-Based Methods to Determine Optimal Well Spacing and Economics in Shale Gas Reservoir with Complex Natural Fractures

Energies ◽

10.3390/en13153965 ◽

2020 ◽

Vol 13 (15) ◽

pp. 3965

Author(s):

Cheng Chang ◽

Chuxi Liu ◽

Yongming Li ◽

Xiaoping Li ◽

Wei Yu ◽

...

Keyword(s):

Shale Gas ◽

History Matching ◽

Net Present Value ◽

Hydraulic Fractures ◽

Natural Fractures ◽

Gas Reservoirs ◽

Gas Reservoir ◽

Shale Gas Reservoir ◽

Shale Gas Reservoirs ◽

Well Spacing

In order to account for big uncertainties such as well interferences, hydraulic and natural fractures’ properties and matrix properties in shale gas reservoirs, it is paramount to develop a robust and efficient approach for well spacing optimization. In this study, a novel well spacing optimization workflow is proposed and applied to a real shale gas reservoir with two-phase flow, incorporating the systematic analysis of uncertainty reservoir and fracture parameters. One hundred combinations of these uncertainties, considering their interactions, were gathered from assisted history matching solutions, which were calibrated by the actual field production history from the well in the Sichuan Basin. These combinations were used as direct input to the well spacing optimization workflow, and five “wells per section” spacing scenarios were considered, with spacing ranging from 157 m (517 ft) to 472 m (1550 ft). An embedded discrete fracture model was used to efficiently model both hydraulic fractures and complex natural fractures non-intrusively, along with a commercial compositional reservoir simulator. Economic analysis after production simulation was then carried out, by collecting cumulative gas and water production after 20 years. The net present value (NPV) distributions of the different well spacing scenarios were calculated and presented as box-plots with a NPV ranging from 15 to 35 million dollars. It was found that the well spacing that maximizes the project NPV for this study is 236 m (775 ft), with the project NPV ranging from 15 to 35 million dollars and a 50th percentile (P50) value of 25.9 million dollars. In addition, spacings of 189 m (620 ft) and 315 m (1033 ft) can also produce substantial project profits, but are relatively less satisfactory than the 236 m (775 ft) case when comparing the P25, P50 and P75 values. The results obtained from this study provide key insights into the field pilot design of well spacing in shale gas reservoirs with complex natural fractures.

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