Schlumberger Technology Corporation

Jeff Spath

Raj Banerjee

Tarek M Habashy

Michael Thambynayagam

R K Michael Thambynayagam

Anthony Frank Veneruso

Aria Abubakar

Roger Griffiths

Andrew John Carnegie

Joseph Ayoub

Luca Ortenzi

Gregory P Grove

Joe M Mach

A method of upscaling for reservoir simulation is disclosed, comprising: inverting a set of deep reading measurements constrained by upscaled multi-well data, and, in response to the inverting step, upscaling for reservoir simulation.

Method for upscaling a reservoir model using deep reading measurements

A method is disclosed for building a predictive or forward model adapted for predicting the future evolution of a reservoir, comprising: integrating together a plurality of measurements thereby generating an integrated set of deep reading measurements, the integrated set of deep reading measurements being sufficiently deep to be able to probe the reservoir and being self-sufficient in order to enable the building of a reservoir model and its associated parameters; generating a reservoir model and associated parameters in response to the set of deep reading measurements; and receiving, by a reservoir simulator, the reservoir model and, responsive thereto, generating, by the reservoir simulator, the predictive or forward model.

Method for reservoir characterization and monitoring including deep reading quad combo measurements

A method is disclosed for modeling a first reservoir while drilling a wellbore into a corresponding second reservoir, the first reservoir having a plurality of stations, comprising: determining a plurality of values of net present value corresponding, respectively, to the plurality of stations of the first reservoir; and drilling the wellbore into the corresponding second reservoir in accordance with the plurality of values of net present value.

Method for determining a set of net present values to influence the drilling of a wellbore and increase production

In a Digital Pressure Derivative Technique (DPDT) which uses convolution, an input signal including noise is convolved with a wavelet to produce an output signal which is a derivative of the input signal and which is substantially devoid of the noise. The DPDT can be used in many applications; however, in one such application, a pressure signal from a wellbore including noise is convolved with a special wavelet to produce an output signal which is the derivative of the input pressure signal, the output signal being substantially devoid of the noise. The wavelet is made from the unit sample response of a bandlimited, optimum linear phase Finite Impulse Response digital differentiator. The DPDT technique enables analysts to customize the differentiation based on the quality of the measured data without having to use traditional, subjective smoothing algorithms. The results presented here, in connection with the one such application mentioned above, bring significant improvements to the diagnosis and interpretation of pressure transient data, and interpretation techniques that utilize higher-order derivatives are now practical.

Digital pressure derivative method and program storage device

A method is presented to estimate the productivity index, PI, and the well condition, s, of a pumping well utilizing the knowledge of pump runtime versus downtime. Runtime and downtime may be constantly and automatically recorded and transmitted to a central location. A model runtime is computed assuming the two unknowns, PI and s. The model is then compared with the actual runtime data. A nonlinear optimization technique is used to search for the unknown parameters such that the differences between the measured data and the numerically simulated data are minimized in a least-squares fashion. The proposed estimation procedure is an economical and accurate method for monitoring the behavior of a well resevoir system during runtime.

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Missouri City, TX, United States of America

Jeff Spath