TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractA fine-scale full-field multimillion cell geologic model of a giant Saudi Arabian carbonate reservoir has been successfully conditioned to a thirty-year production/injection history. This represents the first worldwide case study of the technique for such a complicated reservoir with extensive flow history. The initial geologic model was created based on logderived porosity data, facies information, and a 3-d stochastic seismic inversion model. It was generated geostatistically using a sequential Gaussian simulation with collocated cokriging algorithm. It was desired to condition this initial model based on a thirty-year injection/production history.Fast streamline simulations were performed on the geological porosity and permeability models using field injection/production constraints.The flow conditioning algorithm iteratively modified the permeability field such that simulated injection/production behavior more closely approached the supplied injection/production history. The algorithm uses analogies to seismic travel time and tomography, involving iterative linearization of the time-offlight expression about a known initial model based on static data.The final flow conditioned model was then validated using more thorough streamline simulations verifying improvements in the match of the simulated injection/production response to that of the historical response. Additionally, inspection of the difference between the initial and the flow-conditioned final model showed permeability modifications followed a strong directional trend consistent with well-known geological trends. Results from the final conditioned models showed more than 30% improvement in terms of history-matching than that of initial model.
Advancements in numerical well testing packages in interpreting pressure transient behavior of complex well geometries and reservoir structures have lead to an improved understanding of the multi-scale heterogeneity encountered in dual-porosity dual-permeability (DPDP) reservoirs. This paper demonstrates the power of numerical well testing models in handling conceptual cases of increasing complexity in dual-porosity dual-permeability (DPDP) reservoirs where a high permeability matrix system interact with super-k intervals, fractures, and faults systems with different levels of complexity. Numerical well test models are built using data from multiple scales and sources (image logs, flowmeter responses (PLTs), petrophysical logs (FALs), and seismic attributes) to match pressure transient responses of wells completed in dual-porosity dual-permeability reservoirs. Six generalized conceptual cases are presented in this paper; a vertical power water injector that initiated induced fractures due to injection above fracture pressure, a vertical well near an area of intersecting faults, a 40-degree deviated well intersecting diffuse fracture network, a deviated well near a conductive fracture corridor, a horizontal well intersecting a finite conductivity fracture, and a horizontal well intersecting an infinite conductivity fracture. An integrated approach was used to match the pressure responses in all cases. Experience shows that the most representative well test solution comes from a thorough integration of well-test data with all available static and dynamic data (e.g. image logs, flowmeter responses (PLTs), geophysical, and petro-physical data). In addition, inclusion of pressure transient responses of offset wells in the area understudy as part of the analysis is crucial in choosing the most representative well test model. In summary, this paper provides guidance and best practice to reservoir engineers in building numerical well test models to analyze fracture and matrix responses in dual-porosity dual-permeability (DPDP) reservoirs. Conceptual cases emphasize integration of multiple sources of static and dynamic data to model different well responses.
Detailed compositional simulation of a giant reservoir with many components is not practical. However, detailed multimillion cell black oil simulation of giant reservoirs is now quite feasible. In this work we apply an efficient method to generate the compositional rates from a black oil simulation of the giant Shaybah field. In situations where the reservoir recovery mechanism is not dominated by compositional effects, an Equation of State (EOS) based stream conversion method can be used. This stream conversion method relies on the fact that when laboratory PVT data measured on available well stream compositions are used to generate the black oil PVT tables, some of the compositional information is lost. The stream conversion model retains this valuable compositional information and applies it to each producing well completion in the black oil simulation at every time step. As proof of concept, the stream conversion method was applied to a black oil simulation and to a limited (eightcomponent) compositional simulation to generate a 17component compositional stream and the results were compared to the respective full EOS compositional simulation for a relatively small sector (250,000 cells) of the giant Shaybah field. The compositional stream rates are in excellent agreement with the stream converted black oil results. As would be expected, the computational costs of using the EOS based compositional simulator (with 17 components) is in excess of 40 times the black oil simulation time for the small sector model. In general, the stream conversion method can be used to generate the dynamically varying compositional streams from any black oil simulation for use in the design and operation of surface facilities and in calculating the amounts of a certain cut (e.g. NGL) from the production streams.
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