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Tight reservoir simulation study under geological and technological uncertainty

UDK: 622.276.1/.4:553.98Н.П.
DOI: 10.24887/0028-2448-2018-10-60-63
Key words: multivariant simulation, geological and technological uncertainty, history matching, probability analysis, tight reservoirs, forecast simulation
Authors: D.R. Nurliev (RN-UfaNIPIneft LLC, RF, Ufa), I.I. Rodionova (RN-UfaNIPIneft LLC, RF, Ufa), E.P. Viktorov (RN-UfaNIPIneft LLC, RF, Ufa), M.A. Shabalin (RN-UfaNIPIneft LLC, RF, Ufa), G.A. Makeev (RN-UfaNIPIneft LLC, RF, Ufa), N.O. Novikov (RN-UfaNIPIneft LLC, RF, Ufa), D.D. Suleimanov (RN-UfaNIPIneft LLC, RF, Ufa)

World oil industry current tendency is a transition to development tight reserves. In order to take into account all possible risks related to different kind of geological and technological uncertainties to help make a decision about project economic efficiency, we propose to utilize multivariate simulation for production forecast. Based on actual data from drilled out zones in deep-water part of clinoforms three types of sections were selected. A geological uncertainty those sections is determined by distance from a source of conglomerate. Digital models for each type were created with keeping major morphological features of associated sand bodies. A pool of simulation models was created by variating geological parameters (absolute permeability, net thickness, Corey coefficients at oil relative permeability curves, original oil in place) and technological parameters (fracture half length, fracture height, fracture conductivity). A process of creating multiple models was accomplished by program module RExLab and Rosneft’s program software complex RN-KIM. Latin hypercube algorithm was applied for generating approximately 5000 models for each type of geology. Out of calculated models were selected cases matching actual liquid production and water cut. The selected cases were being used for evaluating technical and economic efficiency of a development pattern within forecast period. As a result of calculation was given Recovery Factor probability distribution function and cumulative NPV per development areas which allow to accomplish an economical project assessment out of P10/P50/P90 perspectives.

References

1. Baykov V.A., Bochkov A.S., Yakovlev A.A., Accounting of nonhomogeneity in Priobskoye field geological modeling and simulation (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2011, no. 5, pp. 50–54.

2. Belyakov E.O., Teploukhov V.M., Using a stochastic model of the pore space connectedness for describing filtration-capacitive properties of Priobskoye field AS9-12 layer (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2010, no. 12, pp. 34–38.

3. Burkholder K., Coopersmith E., Schulze J., Appraisal excellence in unconventional reservoirs, SPE 162776-MS, 2012.

4. Ekeoma E., Appah D.A., Latin hypercube sampling for gas reserves, SPE 128349-MS, 2009.

5. Galeev R.R., Zorin A.M., Kolonskikh A.V. et al., Optimal waterflood pattern selection with use of multiple fractured horizontal wells for development of the low-permeability formations (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2013, no. 10, pp. 62–65.

World oil industry current tendency is a transition to development tight reserves. In order to take into account all possible risks related to different kind of geological and technological uncertainties to help make a decision about project economic efficiency, we propose to utilize multivariate simulation for production forecast. Based on actual data from drilled out zones in deep-water part of clinoforms three types of sections were selected. A geological uncertainty those sections is determined by distance from a source of conglomerate. Digital models for each type were created with keeping major morphological features of associated sand bodies. A pool of simulation models was created by variating geological parameters (absolute permeability, net thickness, Corey coefficients at oil relative permeability curves, original oil in place) and technological parameters (fracture half length, fracture height, fracture conductivity). A process of creating multiple models was accomplished by program module RExLab and Rosneft’s program software complex RN-KIM. Latin hypercube algorithm was applied for generating approximately 5000 models for each type of geology. Out of calculated models were selected cases matching actual liquid production and water cut. The selected cases were being used for evaluating technical and economic efficiency of a development pattern within forecast period. As a result of calculation was given Recovery Factor probability distribution function and cumulative NPV per development areas which allow to accomplish an economical project assessment out of P10/P50/P90 perspectives.

References

1. Baykov V.A., Bochkov A.S., Yakovlev A.A., Accounting of nonhomogeneity in Priobskoye field geological modeling and simulation (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2011, no. 5, pp. 50–54.

2. Belyakov E.O., Teploukhov V.M., Using a stochastic model of the pore space connectedness for describing filtration-capacitive properties of Priobskoye field AS9-12 layer (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2010, no. 12, pp. 34–38.

3. Burkholder K., Coopersmith E., Schulze J., Appraisal excellence in unconventional reservoirs, SPE 162776-MS, 2012.

4. Ekeoma E., Appah D.A., Latin hypercube sampling for gas reserves, SPE 128349-MS, 2009.

5. Galeev R.R., Zorin A.M., Kolonskikh A.V. et al., Optimal waterflood pattern selection with use of multiple fractured horizontal wells for development of the low-permeability formations (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2013, no. 10, pp. 62–65.



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