Weatherford Technology Holdings, LLC

Precision Energy Services, Inc.

Halliburton Energy Services, Inc.

Hamed Chok

Jess V Ford

Jeffery James Hemsing

Endre Anderssen

Christopher Michael Jones

Bin Dai

Jonas Toelke

James Anthony Whetton

Peter Elkington

Andre Anderssen

Simon N Hughes

Michael C Dix

Christopher N Smith

A method comprises receiving a measurement of a pressure in a subsurface formation at a number of depths in a wellbore formed in the subsurface formation across a sampling depth range of the subsurface formation to generate a number of pressure-depth measurement pairs and partitioning the sampling depth range into a number of fluid depth ranges. The method comprises performing a fitting operation over each of the number of fluid depth ranges to determine a fluid gradient for the type of the reservoir fluid for each of the number of fluid depth ranges. The method comprises generating a solution set of one or more solutions based on the fluid gradient of the reservoir fluid for each of the number of fluid depth ranges determined from performing the fitting operation, wherein each solution defines a partitioning of the sampling depth range and the fluid gradient of each fluid depth range.

Determination of location and type of reservoir fluids based on downhole pressure gradient identification

Methods and systems are described for estimating the level of contamination of downhole fluid and underlying composition using physical property measurements, and mathematical modeling of contamination functions and fluid property mixing laws. The proposed approaches enable computation of estimates of the pumping time needed to achieve a certain contamination threshold level and the determination of whether or not sampling is appropriate at the current point in time based on the predicted compositional properties of the formation fluid.

Real-time fluid contamination prediction using bilinear programming

Analysis evaluates formation fluid with a downhole tool disposed in a borehole. A plurality of possible constituents is defined for the formation fluid, and constraints are defined for the possible constituents. The constraints can include boundary constraints and constraints on the system's dynamics. The formation fluid is obtained from the borehole with the downhole tool over a plurality of time intervals, and density of the obtained formation fluid is obtained at the time intervals. To evaluate the fluid composition, a state probability distribution of the possible constituents of the obtained formation fluid at the current time interval is computed recursively from that at the previous time interval and by assimilating the current measured density of the obtained formation fluid in addition to the defined boundary/dynamic constraints. The probabilistic characterization of the state of the possible constituents allows, in turn, the probabilistic inference of formation properties such as contamination level and GOR.

Real-time determination of formation fluid properties using density analysis

A method is disclosed of inverting three dimensional fluid property distribution. The method includes comparing a detected NMR signal with a plurality of modeled signal values derived from precomputed values of NMR signal contribution values at prechosen (T, T, D) value tuples; identifying one or more modeled signals satisfying domain constraints and in respect of which an objective function involving a respective detected NMR signal and a modeled signal is optimized; selecting one or more of the solutions resulting in optimized objective; and using each selected optimized solution to characterize the one or more properties of fluid in the formation. The method also includes processing the resulting solutions as e.g. graphical or tabular data. Also disclosed is apparatus for performing the method.

Method of and apparatus for inverting three-dimensional fluid property distribution over the (T1,T2,D)domain from NMR measurements

Systems and methods for optimizing bed boundary locations and prediction of associated petrophysical property values of a wellbore in a reservoir formation are disclosed. To optimize bed boundary locations and predict petrophysical property values, a process can be used in which a high resolution wellbore log is used to get an initial estimate of boundary positions. The initial estimate can then be used to constrain a joint optimization of bed positions and associated petrophysical property values. Such a process automates both the generation of a single set of boundaries that are common to all measurements and the prediction of petrophysical properties via joint inversion. The same inversion process can be used to derive quality indicators on the corrected bed boundary locations and the petrophysical property values.

System and method for joint inversion of bed boundaries and petrophysical properties from borehole logs

Systems and methods for predicting fluid composition in a reservoir formation are disclosed. To accurately and efficiently predict the composition of a sample fluid, an absorption spectrum is obtained over a prefixed set of wavelength channels and a set of basis spectra is received for all constituents of interest in the sample fluid. A set of a-priori constraints on the obtained absorption spectrum and constituent properties to be determined is then obtained, before calculating a joint probability distribution of all constituents of interest based on the obtained spectrum, basis spectra and a-priori constraints. In one embodiment, qualitative states are also computed for all constituents of interest based on the obtained spectrum, basis spectra and a-priori constraints.

System and method for fluid composition characterization

Clustering analysis is used to partition data into similarity groups based on mathematical relationships between the measured variables. These relationships (or prototypes) are derived from the specific correlation required between the measured variables (data) and an environmental property of interest. The data points are partitioned into the prototype-driven groups (i.e., clusters) based on error minimization. Once the data is grouped, quantitative predictions and sensitivity analysis of the property of interest can be derived based on the computed prototypes. Additionally, the process inherently minimizes prediction errors due to the rigorous error minimization during data clustering while avoiding overfitting via algorithm parameterization. The application used to demonstrate the power of the method is pressure gradient analysis.

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Houston, TX, United States of America

Hamed Chok