Climate conditions affect winter heating demand in areas that experience harsh winters. Skillful energy demand prediction provides useful information that may be a helpful component in ensuring a reliable energy supply, protecting vulnerable populations from cold weather, and reducing excess energy waste. Here, we develop a statistical model that predicts winter seasonal energy consumption over the United Kingdom using a multiple linear regression technique based on multiple sources of climate information from the previous fall season. We take the autumn conditions of Arctic sea-ice concentration, stratospheric circulation, and sea-surface temperature as predictors, which all influence North Atlantic oscillation (NAO) variability as reported in a previous study. The model predicts winter seasonal gas and electricity consumption two months in advance with a statistically significant correlation between the predicted and observed time series. To extend the analysis beyond the relatively short time scale of gas and electricity data availability, we also analyze predictability of an energy demand proxy, heating degree days (HDDs), for which the model also demonstrates skill. The predictability of energy consumption can be attributed to the predictability of the NAO and the significant correlation of energy consumption with surface air temperature, dew point depression, and wind speed. We further found skillful prediction of these surface climate variables and HDDs over many areas where the NAO is influential, implying the predictability of energy demand in these regions. The simple statistical model demonstrates the usefulness of fall climate observations for predicting winter season energy demand prediction with a wide range of potential applications across energy-related sectors.

The objective of this study is to establish whether sonic data acquired in a cased hole can be used to estimate the material behind a second casing when the annulus between the two sets of casings is fluid filled. We have analyzed full-waveform data acquired using the Schlumberger tool Sonic Scanner for a double casing with a fluid between them, and where the outer annulus, outside the outer casing, might be cement filled or fluid filled. The sonic tool uses a cylindrical array of 104 omnidirectional receivers. The cylindrical array - approximately 4 in. in diameter and 1.8 m (6 ft) long - allows a formal decomposition of the acquired data into quasi-plane waves. Analyzing these plane waves, we have identified subtle but distinct changes in the waveforms. These changes appear to be dependent on the material filling the outer annulus, allowing for the determination of the fill material. The most significant changes relate to the propagating Stoneley waves. The identifications made are confirmed by cement bond log (CBL) analysis done on the exposed outer pipe after pulling the inner pipe. In one instance, for single-casing logging and a clean top-ofcement, like with conventional CBL analysis, we were able to confidently and accurately identify the cement/fluid boundary. In another instance, we were able to identify it as a nondistinct/smooth transition zone. For double-casing logging, we could confidently and accurately identify the cement/fluid boundary behind the second casing. For one case, we have substantiated that this zone has a longer interval of smooth transition from cement to consolidated/unconsolidated barite to a fluid-filled annulus.

We have analyzed ultrasonic flexural data acquired in a North Sea well using a commercial tool optimized for generating such data, and found how one might separate refractions along successive layers of casings, and from these separated refractions, characterize successive pipes and annuli. From the timing of refracted events, we determined the shape of a pipe, and by examining the amplitudes of these refracted events — a measure of the conductance of a pipe wall to transverse movement — we characterized the material in the annuli around a pipe. Data from two separate depth intervals were analyzed, demonstrating that a well plan might not give a sufficiently accurate description of the well. In the deeper interval in which the inner pipe was supposed to be free, we found that the annulus was most likely filled with sedimentation and debris. We could also see that the inner 7 in tubing was touching, or nearly touching, the outer 9⅝ in casing over the entire interval. For the shallower interval, below a certain depth, we saw that the 7 in tubing appeared to be touching the 9⅝ in casing approximately every 7–8 m. From measurements inside the 7 in tubing, we estimated the deformation of the outer 9⅝ in casing to be up to a maximum of nearly 5 cm, meaning that the minimum inner diameter of the outer pipe was close to the outer diameter of the inner pipe. Whereas some features revealed by the analyses were “good-to-know” — such as minor pipe deficiencies and deformations — other features might be critical for planned overhaul, or for operations related to abandonment, e.g., touching points were potential sticking points. Knowing where they were may be critical in determining the best depth to cut a pipe before pulling.

Summary The San Andreas Fault Observatory at Depth (SAFOD) is a deep borehole observatory constructed to investigate the source of recurring earthquakes at a specific location on the San Andreas Fault. Numerous geophysical datasets were acquired at the SAFOD site to characterize the subsurface environment. Among these, we collected three independent seismic datasets to characterize the local geologic structure. These datasets include VSP, drill-bit noise, and earthquake recordings. Initially, interferometric deconvolution was applied to the drill-bit noise using Schlumberger patented methods to determine the reference signal from rig-based receivers. Next, it was applied in a modified processing flow to determine the reference signal from downhole receivers. Finally, as a result of the application of the technique to earthquake data, we obtained high resolution imaging of the San Andreas Fault at depths exceeding 2.5 km. In conjunction with the other seismic datasets collected at the site, we interpret the complex system of faults and fractures to constitute a flower structure which is partially exposed at Middle Mountain northeast of the SAFOD site.

ABSTRACTThis paper reports measurements of static and dynamic elastic properties plus compressive strength performed on a block of calcareous mudstone retrieved from an exploration well. Measurements of mechanical properties indicate that the mudstone is anisotropic with respect to all three properties. A detailed analysis of the elastic moduli computed using small unload reload cycles and simultaneous ultrasonic wave velocities shows both strong anisotropy and strong anelasticity. Surprisingly, the measurements are consistent with a mathematical description of a special type of anisotropic linear viscoelastic medium that is obtained by adding a set of compliant elements (e.g., contacts between clay particles, kerogen lenses, or micro‐fractures) to an isotropic viscoelastic solid. This medium is fully characterized by density plus four parameters defining the viscoelastic solid and the excess normal compliance associated with the compliant elements. The mathematical model predicts a full set of parameters characterizing a transversely isotropic medium with a vertical axis of symmetry (a ‘tiv’ medium) for both low‐ and high‐strain rate behaviour.

We investigate the applicability of an array-conditioned deconvolution technique, developed for analysing borehole seismic exploration data, to teleseismic receiver functions and data pre-processing steps for scattered wavefield imaging. This multichannel deconvolution technique constructs an approximate inverse filter to the estimated source signature by solving an overdetermined set of deconvolution equations, using an array of receivers detecting a common source. We find that this technique improves the efficiency and automation of receiver function calculation and data pre-processing workflow. We apply this technique to synthetic experiments and to teleseismic data recorded in a dense array in northern Canada. Our results show that this optimal deconvolution automatically determines and subsequently attenuates the noise from data, enhancing P-to-S converted phases in seismograms with various noise levels. In this context, the array-conditioned deconvolution presents a new, effective and automatic means for processing large amounts of array data, as it does not require any ad-hoc regularization; the regularization is achieved naturally by using the noise present in the array itself.