Response surface method for assessing energy production from geopressured geothermal reservoirs

Ansari, Esmail; Hughes, Richard G.

doi:10.1186/s40517-016-0057-5

Cited by 15 publications

(3 citation statements)

References 18 publications

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“…Setting groups 20 and 23 to 0 results in elimination of the P wf from the scaling parameters (45,47,48,61). We substitute these scaling factors (Eqns (A27)-(A29)) into the above 25 dimensionless groups to obtain the following set of dimensionless numbers, which are no longer arbitrary.…”

Section: Discussionmentioning

confidence: 99%

Response surface modeling of CO₂dynamic storage efficiency factor in high permeability thick sandstones

2019

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In order to utilize as much of the pore space for CO2 storage in high permeability thick saline aquifers, it is vital to investigate the interactions of injected CO2 with formation brine and rock. In order to quantify the displacement process, we investigate the dynamic storage efficiency factor (DSEF) for saline aquifers where pressure increase is minimal during the injection phase. Dimensionless numbers are derived from basic governing equations, constitutive equations, initial and boundary conditions using the inspection analysis. Then using the Hammersley sequence sampling, 178 numerical experiments are designed, and a compositional reservoir simulator is used to perform these simulations. In the next step, response surface regression analysis is used to establish a relationship between DSEF obtained from the numerical simulations and the corresponding dimensionless numbers. The simulation results show that for the studied conditions the underground dynamics is mostly influenced by the gravity number, followed by effective aspect ratio and dip numbers. The results from the response surface regression analysis are used to develop a correlation, which can be used to estimate the dynamic CO2 storage capacity of relevant zones. This study provides quantitative measures for the different competing mechanisms involved in underground displacement of fluids in CO2 geological storage, which can serve as a useful tool during planning phase of storage projects. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Response surface modeling of CO₂dynamic storage efficiency factor in high permeability thick sandstones

2019

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“…Chen et al [14] used a multivariate adaptive regression spline technique to determine the optimal design of geothermal production operation in USA. Ansari et al [15] used polynomial and kriging response surfaces, and they showed that the proxy models can be efficiently used to construct produced energy distribution from the geothermal parameters; distributions. Also for EGS, surrogates have been employed by Asai et al [16] to determine optimal well placement.…”

Section: Surrogate Modelling For Geothermal Applicationsmentioning

confidence: 99%

Optimisation of heat recovery from low-enthalpy aquifers with geological uncertainty using surrogate response surfaces and simple search algorithms

Babaei

Norouzi

Nick

et al. 2022

Sustainable Energy Technologies and Assessments

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“…Several DoE methodologies have been employed in several reservoir simulation studies for constructing proxy models. The prevalent Design of Experiments (DoE) methodologies include fractional factorial design [11], central composite (CC) designs [12,13], D-optimal design [14], Hammersley sequence sampling [15], and Latin Hypercube Design [10,16].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Designed Simulations and Machine Learning to Develop a Surrogate Model for Optimizing the Gas–Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) Process to Improve Clean Oil Production

Al-Mudhafar,

Rao,

Wojtanowicz

2024

Processes

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The Gas and Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) process addresses gas flooding limitations in reservoirs surrounded by infinite-acting aquifers, particularly water coning. The GDWS-AGD technique reduces water cut in oil production wells, improves gas injectivity, and optimizes oil recovery, especially in reservoirs with high water coning. The GDWS-AGD process installs two 7-inch production casings bilaterally. Then, two 2-3/8-inch horizontal tubings are completed. One tubing produces oil above the oil–water contact (OWC) area, while the other drains water below it. A hydraulic packer in the casing separates the two completions. The water sink completion uses a submersible pump to prevent water from traversing the oil column and entering the horizontal oil-producing perforations. To improve oil recovery in the heterogeneous upper sandstone pay zone of the South Rumaila oil field, which has a strong aquifer and a large edge water drive, the GDWS-AGD process evaluation was performed using a compositional reservoir flow model in a 10-year prediction period in comparison to the GAGD process. The results show that the GDWS-AGD method surpasses the GAGD by 275 million STB in cumulative oil production and 4.7% in recovery factor. Based on a 10-year projection, the GDWS-AGD process could produce the same amount of oil in 1.5 years. In addition, the net present value (NPV) given various oil prices (USD 10–USD 100 per STB) was calculated through the GAGD and GDWS-AGD processes. The GDWS-AGD approach outperforms GAGD in terms of NPV across the entire range of oil prices. The GAGD technique became uneconomical when oil prices dropped below USD 10 per STB. Design of Experiments–Latin Hypercube Sampling (DoE-LHS) and radial basis function neural networks (RBF-NNs) were used to determine the optimum operational decision variables that influence the GDWS-AGD process’s performance and build the proxy metamodel. Decision variables include well constraints that control injection and production. The optimum approach increased the recovery factor by 1.7525% over the GDWS-AGD process Base Case. With GDWS-AGD, water cut and coning tendency were significantly reduced, along with reservoir pressure, which all led to increasing gas injectivity and oil recovery. The GDWS-AGD technique increases the production of oil and NPV more than the GAGD process. Finally, the GDWS-AGD technique offers significant improvements in oil recovery and income compared to GAGD, especially in reservoirs with strong water aquifers.

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Response surface method for assessing energy production from geopressured geothermal reservoirs

Cited by 15 publications

References 18 publications

Response surface modeling of CO₂dynamic storage efficiency factor in high permeability thick sandstones

Response surface modeling of CO₂dynamic storage efficiency factor in high permeability thick sandstones

Optimisation of heat recovery from low-enthalpy aquifers with geological uncertainty using surrogate response surfaces and simple search algorithms

Leveraging Designed Simulations and Machine Learning to Develop a Surrogate Model for Optimizing the Gas–Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) Process to Improve Clean Oil Production

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Response surface method for assessing energy production from geopressured geothermal reservoirs

Cited by 15 publications

References 18 publications

Response surface modeling of CO2dynamic storage efficiency factor in high permeability thick sandstones

Response surface modeling of CO2dynamic storage efficiency factor in high permeability thick sandstones

Optimisation of heat recovery from low-enthalpy aquifers with geological uncertainty using surrogate response surfaces and simple search algorithms

Leveraging Designed Simulations and Machine Learning to Develop a Surrogate Model for Optimizing the Gas–Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) Process to Improve Clean Oil Production

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Response surface modeling of CO₂dynamic storage efficiency factor in high permeability thick sandstones

Response surface modeling of CO₂dynamic storage efficiency factor in high permeability thick sandstones