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A new well testing response from lateral cross flow within layers is described. The response occurs when there is extremely low effective vertical permeability in the system at the larger scale. Low vertical permeability actually accentuates the layering and reduces vertical cross flow whilst enhancing lateral cross flow from within-layer heterogeneities. The response is investigated using numerical simulation of flow in end-member models of complex and geologically realistic architecture in high net-to-gross fluvial systems. This ‘ramp’ response is shown to form one member of a family of well test pressure transient responses. The other members of the family include previously-described negative geoskin and geochoke. The use of well test data to characterize these particular types of layered fluvial reservoirs is an important step in the static-dynamic integration of geological and reservoir engineering models.

The process of reservoir history-matching is a costly task. Many available history-matching algorithms either fail to perform such a task or they require a large number of simulation runs. To overcome such struggles, we apply the Gaussian Process (GP) modeling technique to approximate the costly objective functions and to expedite finding the global optima. A GP model is a proxy, which is employed to model the input-output relationships by assuming a multi-Gaussian distribution on the output values. An infill criterion is used in conjunction with a GP model to help sequentially add the samples with potentially lower outputs. The IC fault model is used to compare the efficiency of GP-based optimization method with other typical optimization methods for minimizing the objective function. In this paper, we present the applicability of using a GP modeling approach for reservoir history-matching problems, which is exemplified by numerical analysis of production data from a horizontal multi-stage fractured tight gas condensate well. The results for the case that is studied here show a quick convergence to the lowest objective values in less than 100 simulations for this 20-dimensional problem. This amounts to an almost 10 times faster performance compared to the Differential Evolution (DE) algorithm that is also known to be a powerful optimization technique. The sensitivities are conducted to explain the performance of the GP-based optimization technique with various correlation functions

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