Summaryobjective To assess the efficacy of chloroquine in the treatment of Plasmodium vivax malaria in in Dawei District, southern Myanmar.methods Enrolled patients at Sonsinphya clinic >6 months of age were assessed clinically and parasitologically every week for 28 days. To differentiate new infections from recrudescence, we genotyped pre-and post-treatment parasitaemia. Blood chloroquine was measured to confirm resistant strains.results Between December 2002 and April 2003, 2661 patients were screened, of whom 252 were included and 235 analysed. Thirty-four per cent (95% CI: 28.1-40.6) of patients had recurrent parasitaemia and were considered treatment failures. 59.4% of these recurrences were with a different parasite strain. Two (0.8%) patients with recurrences on day 14 had chloroquine concentrations above the threshold of 100 ng ⁄ ml and were considered infected with chloroquine resistant parasites. 21% of failures occurred during the first 3 weeks of follow-up: early recurrence and median levels of blood chloroquine comparable to those of controls suggested P. vivax resistance.conclusions Plasmodium vivax resistance to chloroquine seems to be emerging in Dawei, near the Thai-Burmese border. While chloroquine remains the first-line drug for P. vivax infections in this area of Myanmar, regular monitoring is needed to detect further development of parasite resistance.

Background: In 2003, artesunate-amodiaquine (AS+AQ) was introduced as the new first-line treatment for uncomplicated malaria in Burundi. After confirmed diagnosis, treatment was delivered at subsidized prices in public health centres. Nine months after its implementation a study was carried out to assess whether children below five years of age with uncomplicated malaria were actually receiving AS+AQ.

The Single Well Tracer Test (SWTT) method has proved to be a reliable technique to measure Remaining Oil saturations (ROS) consistently. The technique is based on the chromatographic separation of two tracers, a tracer that partitions into the oil and that hydrolyses in the reservoir, to generate the second tracer, which remains exclusively in the water. Both tracers are produced through the same well in an interval of a few days and the ROS is determined by means of the difference in their arrival times at the wellhead. The SWTT technique was first implemented within Total in 2007 in the Handil field, Indonesia, and was recently employed by the group for ROS determination in an offshore field located in the Gulf of Guinea in West Africa. This paper focuses on the results of the Single-Well Chemical Tracer Test (SWTT) carried out for this offshore field and how the information was used to better assess the reservoir's current ROS. Total's SWTT interpretation methodology allowed the group to distinguish the presence of moderate to low ROS values in the swept layers of the investigated interval, while there was a higher uncertainty in ROS in non perforated layers. This paper presents the SWTT results obtained in this West African field together with the detailed laboratory campaign that was carried out to complement field SWTT information. The paper also details the SWTT interpretation carried out by Total and the learned lessons obtained from this offshore SWTT application.

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There are two main methods to incorporate uncertainties in the production profiles. The first option relies on multiple geomodels and reservoir simulations that require long engineering time but help to assess accurately the low and high case profiles integrating the main geosciences and reservoir uncertainties. The second option is to use Decline Curve Analysis (DCA) with a reduced engineering time. Nevertheless, this method relies solely on production data, ignoring the geology and various production mechanisms. Process automation and machine learning workflows can then be of great help in this second case but remain generally limited, lacking transverse features for the geology and production mechanisms. In this paper we will present a hybrid approach where the low and high case production profiles were stochastically generated, derived from a deterministic basecase model simulation. This approach was tested on a giant carbonate field in the Middle East with a few million grid cells and hundreds of wells. The study was provided with a basecase history matched model and its associated forecast run. The interest of the method is to save time as the hybrid approach gives faster results compared to existing methodology while including geological and dynamic features to generate low and high cases for production profiles Machine learning based workflows were developed to calculate and extract from 3D timelapse models and 2D results all the important parameters (well features) driving well performance in the history but also in the forecast. Those important features were used to train a machine learning random forest model to predict the cumulative production (Np) of the wells. In addition to the ability to predict the Np, the model also gives the impact of every parameter on the cumulative production, allowing a ranking of the most impacting production mechanisms and geological parameters. The distance to the waterfront at the time the well was drilled was identified as the major parameter impacting the cumulative production; this feature and other key parameters were therefore used to generate the low and high cases. The final outputs from the study were the delivery of the low and high production profiles, taking into consideration the main production mechanisms and geological uncertainties identified and turned into features for the workflow. This work was done thanks to a multidisciplinary team composed of Reservoir engineers and datascientists from the FRF team.