History Matching Flowmeter Data in the Ghawar Field A Combined Facies and Discrete Fracture Reservoir Model J. Voelker

Acknowledgement: Jim Liu, Saudi Aramco

Principal points: the necessity of a super-k characterization generation of combined facies/discrete fracture models use of the well model to simulate discrete fracture flow history matching flowmeter data with probability perturbation

Ghawar Field (Saudi Aramco) largest onshore field in the world 50 years of production mixed clastic/carbonate deposition study area: 8 km x 3 km x 250 ft Meyer, 2000

Study area simulation grid reservoir grid super-k bed grainstone background reservoir grid

The necessity of a super-k characterization abnormally high flow capacity measured at the well, not predicted by inflow performance (kh, skin) flow not correlated with permeability Meyer, 2000

The necessity of a super-k characterization problem: abnormal flow capacity extends beyond the well super-k causes early injection water breakthrough at adjacent producing wells possible producing well abandonment due to inability to shut off water solution: predict the location of super-k final history matched model must have the correct geology, including the essential flow elements of super-k method of solution: maintain the correct geology throughout the history matching process: probability perturbation

Generation of combined facies/discrete fracture models essential super-k elements: grainstone discrete fracture super-k bed Meyer, 2000

Generation of combined facies/discrete fracture models existence of faulting region study area

Generation of combined facies/discrete fracture models the facies model N/G=0.54 N/G=0.38 N/G=0.29 super-k bed model N/G = 0.18 grainstone model super-k bed grainstone background combined model

+ = Generation of combined facies/discrete fracture models stochastic: super-k beds + = super-k beds imbedded into facies model

Generation of combined facies/discrete fracture models Facies alone cannot predict super-k flow Grainstone model Grainstone + super-k bed model 15 equiprobable realizations model incomplete additional flow component required: fractures

Use of the well model to simulate discrete fracture flow fracture flow simulation candidates: pro: good fracture geometry description, no limit on number of fractures con: every new model requires a new flow simulation grid discretization pro: flow simulation grid remains the same con: poor fracture geometry description, limited number of fractures dual porosity pro: easily implemented, grid update not required, no limit on number of fractures con: fracture geometry description almost as good as discretization (but better than DP), new well model

Use of the well model to simulate discrete fracture flow discretization dual porosity well model

Use of the well model to simulate discrete fracture flow most effective placement of one fracture end: at the well blocks implementation west injection well implication: the discrete fracture is near the Peaceman radius of the well, discrete fracture does not intersect the well : no well stimulation b/d/ft

Use of the well model to simulate discrete fracture flow most effective placement of other fracture end: at a super-k bed located in a different pressure regime implementation 1 4 2 5 y y 3 z = 51 x west injection well 1 2 3 4 5

Use of the well model to simulate discrete fracture flow super-k bed / fracture realizations: perturbation of super-k beds results in perturbation of fractures well blocks: conditioning super-k bed blocks:search

History matching flowmeter data with probability perturbation ? objective of history match: super-k predictions

History match: 2 tests, separated by 3 years facies flowmeter fracture History match: 2 tests, separated by 3 years (total 18 tests in 25 years)

History matching flowmeter data with probability perturbation equiprobable realizations Facies ony Facies ony

History matching flowmeter data with probability perturbation probability perturbation results (5 iterations) 4 2 1

History matching flowmeter data with probability perturbation probability perturbation results (5 iterations) 4 5 2 3 1

History matching flowmeter data with probability perturbation probability perturbation results (5 iterations) 4 3 2 1

i=0

i=1

i=2

i=3

i=4

History matching flowmeter data with probability perturbation simulation behavior (2-2.8 GHz, 2 GB) cpu cost per flow simulation: 3 year run, no fractures: ~10 minutes 3 year run, 2 fractures: ~12 minutes 25 year run, no fractures: ~30 minutes 25 year run, 5 fractures: ~100 minutes cpu cost increases significantly with number of fractures, number of connections, and connection transmissibility

Conclusions Future work super-k flow behavior may be described with a combined facies / discrete fracture model discrete fracture flow may be modeled effectively with conventional well models probability perturbation is an effective history matching tool in this case study Future work extend the technique to a larger study area improve the convergence properties of simulations develop well placement strategies

Extra slides

History matching flowmeter data with probability perturbation essential characteristics of the model: discrete fractures connections to depletion/injection regions super-k beds only short fractures

History matching flowmeter data with probability perturbation probability perturbation results 4 3 2 1 4 1 2 3

Analytical characterization of super-k Given a facies reservoir model: facies proportions facies geometry facies well conditioning facies permeability reliable flowmeter data known well damage or stimulation Then: flowmeter data is governed by zone permeability

Analytical characterization of super-k Flowmeter plot Binary facies model Well data layered model

Analytical characterization of super-k Layered reservoir Flowmeter-west injection well Correct facies geology Super-k

The discrete fracture / super-k bed requirement Added elements must: be constituents of the real geology increase well flow capacity exist in constrained vertical intervals Simplest conventional candidate: discrete fractures

Use of the well model to simulate discrete fracture flow maximum fracture connection transmissibility,