Halliburton

About: Halliburton is a company organization based out in Arbroath, United Kingdom. It is known for research contribution in the topics: Casing & Signal. The organization has 11727 authors who have published 18078 publications receiving 255157 citations. The organization is also known as: Halliburton Company.

A Practical Use of Shale Petrophysics for Stimulation Design Optimization: All Shale Plays Are Not Clones of the Barnett Shale

Abstract: The most common fallacy in the quest for the optimum stimulation treatment in shale plays across the country is to treat them all just like the Barnett Shale. There is no doubt that the Barnett Shale play in the Ft. Worth Basin is the “granddaddy” of shale plays and everyone wants their shale play to be “just like the Barnett Shale.” The reality is that shale plays are similar to any other coalbed methane or tight sand play; each reservoir is unique and the stimulation and completion method should be determined based on its individual petrophysical attributes. The journey of selecting the completion style for an emerging shale play begins in the laboratory. An understanding of the mechanical rock properties and mineralogy is essential to help understand how the shale reservoir should be completed. Actual measurements of absorption-desorption isotherm, kerogen type, and volume are also critical pieces of information needed to find productive shale reservoirs. With this type of data available, significant correlations can be drawn by integrating the wireline log data as a tool to estimate the geochemical analysis. Thus, the wireline log analysis, once calibrated with core measurements, is a very useful tool in extending the reservoir understanding and stimulation design as one moves away from the wellbore where actual lab data was measured. A recent study was conducted to review a laboratory database representing principal shale mineralogy and wireline log data from many of the major shale plays. The results of this study revealed some statistically significant correlations between the wireline log analysis and measured mineralogy, acid solubility, and capillary suction time test results for shale reservoirs. A method was also derived to calculate mechanical rock properties from mineralogy. Understanding mineralogy and fluid sensitivity, especially for shale reservoirs, is essential in optimizing the completion and stimulation treatment for the unique attributes of each shale play. The results of this study have been in petrophysical models driven by wireline logs that are common in the industry to classify the shale by lithofacies, brittleness, and to emulate the lab measurement of acid solubility and capillary suction time test. This is the first step in determining if a particular shale is a viable resource, and which stimulation method will provide a stimulation treatment development and design. A systematic approach of validating the wireline log calculations with specialized core analysis and a little “tribal” knowledge can help move a play from concept to reality by minimizing the failures and shortening the learning cycle time associated with a commercially successful project. Introduction Producing methane from shale has been practiced in North America for more than 180 years. The first known well in the U.S. drilled to produce natural gas for commercial purposes was in 1821 outside of Fredonia, N.Y. (2008 www.britannica.com). This well produced from a fractured organic-rich shale through a hand dug well. It was produced for more than 75 years. Production from the Antrim shale in the Michigan Basin started in 1936. Today, there are more than 9,000 wells producing, most of which were drilled after 1987. The Barnett Shale, discovered in 1981, is being produced from more than 8,000 wells today (Wang 2008). Fig. 1 represents the growth of the Barnett Shale play in the Newark, East field in the Ft. Worth basin. The cumulative gas production from this field is more than 4 Tcf. One could characterize the success of this play as: the right market, the right people, and the right technology (Wang 2008). The key technologies for the Barnett Shale success revolve around horizontal drilling and hydraulic fracture stimulation.

Control of particulate flowback in subterranean wells

Abstract: The present invention provides a method of treating a wellbore penetrating a subterranean formation with a treatment fluid whereby fine particulate flowback is reduced or prevented. The method includes the steps of providing a fluid suspension including a mixture of a particulate coated with a tackifying compound, pumping the suspension into a subterranean formation and depositing the mixture within the formation whereby the tackifying compound retards movement of at least a portion of any fine particulate within the formation upon flow of fluids from the subterranean formation through the wellbore. Alternatively, the tackifying compound may be introduced into a subterranean formation in a diluent containing solution to deposit upon previously introduced particulates to retard movement of such particulates and any fines subject to flow with production of fluids from the subterranean formation.

Use of multiattribute transforms to predict log properties from seismic data

Abstract: We describe a new method for predicting well‐log properties from seismic data. The analysis data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs theoretically may be of any type; however, the greatest success to date has been in predicting porosity logs. From the 3-D seismic volume a series of sample‐based attributes is calculated. The objective is to derive a multiattribute transform, which is a linear or nonlinear transform between a subset of the attributes and the target log values. The selected subset is determined by a process of forward stepwise regression, which derives increasingly larger subsets of attributes. An extension of conventional crossplotting involves the use of a convolutional operator to resolve frequency differences between the target logs and the seismic data. In the linear mode, the transform consists of a series of weights derived by least‐squares minimization. In the nonlinear mode, a neural network is trained, using the selected att...

Bayesian Compressive Sensing via Belief Propagation

Abstract: Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear programming or greedy algorithms. We perform approximate Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model. Fast computation is obtained by reducing the size of the graphical model with sparse encoding matrices. To decode a length-N signal containing K large coefficients, our CS-BP decoding algorithm uses O(Klog(N)) measurements and O(Nlog^2(N)) computation. Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models.

Bayesian Compressive Sensing Via Belief Propagation

Abstract: Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear programming or greedy algorithms We perform asymptotically optimal Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model Fast computation is obtained by reducing the size of the graphical model with sparse encoding matrices To decode a length-N signal containing K large coefficients, our CS-BP decoding algorithm uses O(K log(N)) measurements and O(N log2(N)) computation Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models