Summary Answering general questions such as "Where is the oil?," "How much oil is there?," and "Can we extract it?" is a challenging task for a large fractured field in southern Italy. Various studies were conducted to gain more insight into the way oil is distributed in the rock and the producibility of the different structures observable on the cores (matrix, vugs, and fissures). These included the cryogenic scanning electron microscope (CryoSEM) and gas chromatography (GC)-pyrolysis, pore-size-distribution measurements, SEM analysis on thin sections, and a number of nonconventional techniques that were designed specifically for that type of rock. Nuclear magnetic resonance (NMR) imaging was conducted on several whole core samples and the different porosity contributions (microporosity, vugs, and fissures) defined on a 3D basis. An analytical approach based on the percolation theory was used to separate the permeability contributions and define the conditions under which vugs and fissures may form a conducting system. The inputs were distributions of pore throats, throat length, coordination number, fissure orientation, and porosities. Wettability is a key parameter for production estimates, and we used a technique for measuring it in both microporosity and fissures, which makes use of dielectric constant measurements. All the data contributed to our current understanding of the reservoir.

Three-dimensional fracture networks, combined from seismic scale to wellbore scale, can greatly enhance the knowledge of fracture contribution to hydrocarbon storage and flow inside the reservoir. This paper presents some techniques used at ENI-Agip for fracture network simulation at wellbore scale. First, new imaging techniques adopted for enhancing and facilitating the data acquisition from orientated cores will be shown. We will discuss how the fracture data, acquired from the combined analysis of orientated core and wellbore image logs, have to be processed in order to calculate the geometrical parameters of each feature (dip direction, dip, size, terminations) and to classify them according to their filling (oil, water, shale, calcite, etc.). Second, we will illustrate how these kind of data are processed in order to extract the fracture representative parameters needed for stochastic simulation of fracture network at the wellbore scale: spatial distribution along the cored/logged interval, number of fracture sets, representative orientation and statistical distribution of each set, distribution laws of the fracture length and relevant minimum radii and fracture aperture estimate. Fracture porosity evaluation, matrix block size, fracture network connectivity at wellbore scale constitute the outputs of such simulations: they are used to better characterize a fractured reservoir and to describe its behaviour. The synthetic results of the application of such a methodology on a real case (tight packstone/wackestone of carbonate platform from a southern Apennine italian oil reservoir) complete the paper.

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 71741, "Use of an Integrated Approach for Estimating Petrophysical Properties in a Complex Fractured Reservoir: A Case History," by N. Bona, SPE, F. Radaelli, A. Ortenzi, A. De Poli, and C. Peduzzi, ENI/Agip, and M. Giorgioni, SPE, Enterprise Oil Italiana, originally presented at the 2001 SPE Annual Technical Conference and Exhibition, New Orleans, 30 September-3 October.

An in-house genotypic antiretroviral resistance assay was evaluated by testing 32 plasma samples obtained from heavily pretreated human immunodeficiency virus type 1 (HIV-1)-infected patients failing multiple antiretroviral regimens. The same samples were also sent to Virco Laboratories for genotypic (VircoGEN™) and phenotypic (Antivirogram™) resistance analysis. Sequencing results obtained by in-house (HG) and VircoGEN™ (VG) genotyping were concordant for 387 of 400 (96.75%) drug resistance mutations. Genotype-based prediction of drug susceptibility for 13 currently licensed antiretroviral compounds were in agreement in 336 (80.78%) cases, partially concordant in 73 (17.54%) cases and discordant in only seven (1.68%) cases. VG indicated ‘possible resistance’ twice as much as HG. When genotype interpretation was compared with the Antivirogram™ phenotypic data, there were 27 (6.49%) and 23 (5.52%) wrong calls by HG and by VG, respectively. Both assays were more sensitive in detecting drug resistance than drug susceptibility (94.61 vs. 65.19% for HG, 80.84 vs. 56.91% for VG) and more specific in detecting drug susceptibility than drug resistance (93.62 vs. 73.49% for HG, 93.62 vs. 80.32% for VG). Rule-based algorithms can reliably interpret genotypic data obtained from most heavily pretreated patients. However, occasional genotypic patterns may be erroneously interpreted without resistance phenotyping.

To the Editor: Lopinavir, coformulated with a boosting dose of ritonavir, (LPV/r) has been recently licensed as a new protease inhibitor (PI) with excellent pharmacokinetic properties (1). Due to the achievement of high blood levels, there is a high genetic barrier to clinical resistance to LPV/r. Indeed, as many as 11 mutations at HIV-1 protease codons 10, 20, 24, 46, 53, 54, 63, 71, 82, 84 and 90 have been reported to contribute to LPV/r resistance, and at least 8 of these appear to be required for significant clinical resistance (2). Although preliminary LPV/r efficacy studies have yielded promising results, especially on drug-naive subjects (3–5), the role of LPV/r as a salvage therapy for heavily experienced patients in clinical practice has not yet been fully elucidated. We report an observational study of the first 41 patients shifted to an LPV/r-including regimen and reaching at least 16 weeks (mean ± SD, 23 ± 5 weeks) of LPV/r therapy in our area. The subjects were pretreated with a median of 5 (range 4–6) nucleoside reverse transcriptase inhibitors (NRTIs), 1 (range 1–2) nonnucleoside reverse transcriptase inhibitors (NNRTIs) and 4 (range 2–5) PIs. The median number of different treatment regimens used before commencing LPV/r therapy was 8 (range 4–13), including dual NRTI treatments, and the median HIV-1 RNA load and CD4 counts at baseline were 5.29 (range 3.79–7.00) log10 copies/mL and 138 (range 4–689) cells/ L, respectively. A total of 26 (63.4%) and 30 (73.2%) subjects had >10 HIV-1 RNA copies/mL and <200 CD4 cells/ L, respectively. Genotypic antiretroviral resistance analysis (6) at baseline revealed a median number of 6 (range 0–10) NRTI resistance mutations, 1 (range 0–4) NNRTI resistance mutations, and 2 (range 0–4) and 5 (range 1–8) primary and accessory PI resistance mutations, respectively. There was only one case without resistance mutations, possibly resulting from multiple treatment interruptions before starting LPV/r. The number of patients harboring virus with at least one primary NRTI, NNRTI, and PI resistance mutations was 38 (92.7%), 28 (68.3%), and 35 (85.4%), respectively. Five (12.2%) subjects harbored virus with a 69S-XX insertion or Q151M complex NRTI class resistance, and more than two primary PI resistance mutations were present in virus from 20 (48.8%) individuals. Except for two subjects in whom LPV/r was associated with one NRTI and one NNRTI (efavirenz), the new regimen combined LPV/r with two NRTIs, mainly stavudine/didanosine (16 cases) and stavudine/lamivudine (8 cases). However, based on baseline RT genotype, only 9 (22.0%) and 18 (49.3%) patients were administered 2 and 1 reverse transcriptase inhibitors expected to retain activity against their viral isolate, respectively, according to the on-line drug resistance interpretation system available at the Stanford University web site (http://hivdb. stanford.edu/). Based on a standardized questionnaire, the selfreported adherence to treatment was optimal in this cohort. Overall, there was a significant response to LPV/r therapy both in terms of HIV-1 RNA levels (5.25 ± 0.68 log10 at baseline vs. 3.94 ± 1.27 log10 at the end of follow-up; p < .001, paired t test) and in terms of CD4 cell counts (166 ± 153 vs. 225 ± 193; p .009). Interestingly, changes in HIV-1 RNA levels and CD4 cell counts were comparable in subjects with <5 log10 HIV-1 RNA copies/mL (−1.30 log10 copies/mL and +45 cells/ L) and >5 log10 HIV-1 RNA copies/mL (−1.32 log10 copies/mL and +67 cells/ L), respectively. HIV-1 RNA levels were suppressed to <500 copies/mL and <50 copies/mL in 13 (31.7%) and 9 (21.9%) patients, respectively. Table 1 shows the evolution of PI resistance mutations under the selective pressure of LPV/r (GenBank accession numbers AF493336 to AF493415). There was a statistically significant increase in the prevalence of total PI resistance mutations (me-

Abstract Answering general questions such as "Where is the oil? How much oil is there? Can we extract it?" is a challenging task for a large fractured reservoir in Southern Italy. Various studies were conducted to gain more insight into the way the oil is distributed in the rock and the producibility of the different structures observable at the core scale (matrix, vugs, fissures). These included CryoSEM and GC-pyrolysis, pore size distribution measurements, SEM analysis on thin sections, and a number of non-conventional techniques that were specifically designed for that type of rock. NMR imaging was conducted on several whole core samples and the different porosity contributions (microporosity, vugs, and fissures) defined on a 3-D basis. An analytical approach based on the percolation theory was used to separate the permeability contributions and define the conditions under which vugs and fissures may form a conducting system. The inputs were distributions of pore throats, throat length, coordination number, fissure orientation and porosities. Wettability is a key parameter for production estimates, and we utilised a technique for measuring it in both microporosity and fissures, which makes use of dielectric constant measurements. All the data contributed to our current understanding of the reservoir.