scholarly journals Practical Aspects of Upscaling Geocellular Geological Models for Reservoir Fluid Flow Simulations: A Case Study in Integrating Geology, Geophysics, and Petroleum Engineering Multiscale Data from the Hunton Group

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1604 ◽  
Author(s):  
Benmadi Milad ◽  
Sayantan Ghosh ◽  
Roger Slatt ◽  
Kurt Marfurt ◽  
Mashhad Fahes
Keyword(s):  
History Matching ◽  
Grid Cell ◽  
Natural Fracture ◽  
Petroleum Engineering ◽  
Scale Model ◽  
Thin Sections ◽  
Flow Simulations ◽  
Cumulative Production ◽  
Geologic Model

Optimal upscaling of a high-resolution static geologic model that reflects the flow performance of the reservoir is important for reasons such as time and calculation efficiency. In this study, we demonstrate that honoring reservoir heterogeneity is critical in predicting accurate production and reducing the time and cost of running reservoir flow simulations for the Hunton Group carbonate. We integrated three-dimensional (3D) seismic data, well logs, thin sections, outcrops, multiscale fracture studies, discrete fracture networks, and geostatistical methods to create a 100 × 150 × 1 ft gridded representative geologic model. We calibrated our gridded porosity and permeability parameters, including the evaluation of fractures, by history matching the simulated production rate and cumulative production volumes from a baseline fine-scale model generated from petrophysical and production data obtained from five wells. We subsequently reperformed the simulations using a suite of coarser grids to validate our property upscaling workflow. Compared to our baseline history matching, increasing the horizontal grid cell sizes (i.e., horizontal upscaling) by factors of 2, 4, 8, and 16 results in cumulative production errors ranging from +0.5% for two time (2×) coarser to +74.22% for 16× coarser. The errors associated with vertical upscaling were significantly less, i.e., ranging from +0.5% for 2× coarser to +10.8% for 16× coarser. We observed higher production history matching errors associated with natural fracture size. Results indicate that greater connectivity provided by natural fracture length has a larger effect on production compared to the higher permeability provided by larger apertures. We also estimated the trade-off between accuracy and run times using two methods: (a) using progressively larger grid cell sizes; (b) applying 1, 5, 10, and 20 parallel processes. Computation time reduction in both scenarios may be described by simple power law equations. Observations made from our case study and upscaling workflow may be applicable to other carbonate reservoirs.

A Novel Workflow for Optimizing Well Placement Under Geomechanical Constraints: A Case Study

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Cheng An ◽  
Peng Zhang ◽  
Amanveer Wesley ◽  
Gaetan Bardy ◽  
Kevin Hall ◽  
...  
Keyword(s):  
Stress Field ◽  
Three Dimensional ◽  
Production Performance ◽  
Natural Fracture ◽  
Petrophysical Properties ◽  
Reservoir Model ◽  
Differential Stress ◽  
Well Placement ◽  
Flow Simulations

Abstract A novel workflow to optimize well placement using geomechanical constraints is introduced to maximize production performance, reduce excessive simulation runs, and minimize drilling constraints by considering the local stress field and the petrophysical properties in a given reservoir. A case study is presented for optimization of horizontal well placement in the Monterey Formation of Miocene Age in California. First, a three-dimensional reservoir model of formation pressure, in situ stresses, petrophysical and rock properties were built from available petrophysical and well log data. Second, numerical modeling using material point method (MPM) was applied to generate the differential stress field, taking into consideration a three-dimensional natural fracture network in the reservoir model. Third, an optimization algorithm which incorporates petrophysical properties, natural fracture distribution, differential stresses, and mechanical stability was used to identify the best candidate locations for well placement. Finally, flow simulations were conducted to segregate each candidate location where both natural and hydraulic fractures were considered. Statistical methods identify optimal well positions in areas with low differential stress, high porosity, and high permeability. Several candidate locations for well placement were selected and flow simulations were conducted. A comparison of the production performance between the best candidates and other randomly selected well configurations indicates that the workflow can effectively recognize scenarios of optimum well placement. The proposed workflow provides practical insight on well placement optimization by reducing the number of required reservoir simulation runs and maximizing the hydrocarbon recovery.

scholarly journals Case Study: Effect of Natural Fracture on Stimulation Strategy in Keshen Tight Gas

2020 ◽  
Vol 570 ◽  
pp. 062025
Author(s):  
Y Zhang ◽  
Z Chen ◽  
X Yang ◽  
Y Qiao ◽  
Q Teng ◽  
...  
Keyword(s):  
Natural Fracture ◽  
Tight Gas

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

Software Assisted History Matching - A Case Study of a Very Constrained Problem with Intensive Pressure and Saturation Data.

2006 ◽  
Author(s):  
Shawket G. Ghedan ◽  
Adrian P. Gibson ◽  
Ilhan Sener ◽  
Ozgur Eylem Gunal ◽  
Alexander Diab ◽  
...  
Keyword(s):  
History Matching ◽  
Constrained Problem ◽  
Assisted History Matching

Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation

2013 ◽  
Vol 50 ◽  
pp. 4-15 ◽  
Author(s):  
D. Arnold ◽  
V. Demyanov ◽  
D. Tatum ◽  
M. Christie ◽  
T. Rojas ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
History Matching

Extending Production Life of Mature Fields in the North Sea with Multilateral Technology

2021 ◽  
Author(s):  
Matthew Kelsey ◽  
Magnus Raaholt ◽  
Olav Einervoll ◽  
Rustem Nafikov ◽  
Stian Amble
Keyword(s):  
Environmental Impact ◽  
North Sea ◽  
Construction Costs ◽  
The North ◽  
Economic Production ◽  
Successful Case ◽  
Isolation Methods ◽  
Cumulative Production ◽  
The North Sea

Abstract Multilateral technology has for nearly three decades extended the production life of fields in the North Sea by delivering a higher recovery factor supported by the cumulative production of the multiple laterals. Additionally, operators continue to look at methods to reduce the environmental impact of drilling and intervention. Taking advantage of the latest multilateral technology can turn otherwise unviable reservoirs into economically sound targets by achieving a longer field life while minimizing construction costs, risk, and environmental impact. This paper will focus on mature fields in the region that have used multilateral applications for wells that were reaching the end of their life and have been extended to further economic production. This paper discusses challenges faced to provide a multilateral solution for drilling new lateral legs in existing wells where there is a lack of available slots to drill new wells. Additionally, discussion will cover completion designs that tie new laterals into existing production casing. The case study will include discussion of workover operations, isolation methods, and lateral creation systems. The paper focuses on the challenges, solutions, and successful case study of a retrofit multilateral well constructed in the North Sea which extended production life in a mature field by using innovative multilateral re-entry methods. The paper also provides insight as to methodology for continually improving reliability of multilateral installations to maximize efficiencies.

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