A new optimisation procedure for uncertainty reduction by intelligent wells during field development planning

The uncertainty in the produced oil volume can be minimised by substituting intelligent wells (IWs) for conventional wells. A previous study showed that IWs reduce the impact of geological uncertainty on the production forecast (Birchenko, Demyanov et al. 2008). This investigation has now been extended to the “dynamic” parameters (fluid contacts, relative permeabilities, aquifer strength and zonal skin). The efficiency of the IWs in reducing the total production uncertainty due to the reservoir’s dynamic parameters was found to be comparable to that reported for the static parameters. However, this later study identified that the result was strongly dependent on the strategy employed to optimise the field’s performance. Experience has shown that challenges arise while using commercial software for optimisation of a typical, modern field with multiple reservoirs and a complex surface production network. Inclusion of the optimisation algorithm dramatically increases the calculation time in addition to showing stability and convergence problems. This thesis describes the development of a novel method of a reactive control strategy for ICVs that is both robust and computationally fast. The developed method identifies the critical water cut threshold at which a well will operate optimally when on/off valves are used. This method is not affected by the convergence problems which have lead to many of the difficulties associated with previous efforts to solve our non-linear optimisation problem. Run times similar to the (non-optimised) base case are now potentially possible and, equally importantly, the optimal value calculated is similar to the result from the various optimisation software referred to above. The approach is particularly valuable when analysing the impact of uncertainty on the reservoir’s dynamic and static parameters, the method being convergent and independent of the point used to initiate the optimization process. “Tuning” the algorithm’s optimisation parameters in the middle of the calculation is no longer required; thus ensuring the results from the many realisations are comparable

Clinical studies on patients with Sjogren's syndrome Part 2 Characteristics of patients with Sjogren's syndrome and the effect of corticosteroid therapy

We analyzed the clinical and laboratory findings of 160 patients with definite Sjogren's syndrome, and examined the effect of corticosteroid therapy on these patients. One hundred and fifty nine were female, and the average age at the time of diagnosis was 46.2±12.2 years. The past histories disclosed gynecological diseases in 44 patients (29.9%) and appendicitis in 32(21.8%); 10(6.8%) with sinusitis; 10(6.8%) with pleuritis. Complaints of oral dryness and decreased saliva were common in 73.8% of patients. Failure of lacrimation, redness, "film", and increased dental caries were present in 20-30% of patients. In addition, systemic manifestations such as arthralgia, fever, Raynaud's phenomenon, and lymph node swelling were frequent. Abnormalities of Schirmer's test, gum test, histological findings of salivary glands, and of the sialogram were found in 80% of patients. Hyper γ-globulinemia was present in 81%. Anti SS-A antibody, rheumatoid factor, and total antinuclear antibodies were positive in 60-70%. Corticosteroid was effective for patients with the sicca syndrome; it relieved the symptoms of dry eye and dry mouth, and also tended to decrease the inflamation of the salivary glands. Serum γ-globulin and amylase were lower immediately after the corticosteroid treatment

Monte Carlo on the manifold and MD refinement for binding pose prediction of protein–ligand complexes: 2017 D3R Grand Challenge

Manifold representations of rotational/translational motion and conformational space of a ligand were previously shown to be effective for local energy optimization. In this paper we report the development of the Monte-Carlo energy minimization approach (MCM), which uses the same manifold representation. The approach was integrated into the docking pipeline developed for the current round of D3R experiment, and according to D3R assessment produced high accuracy poses for Cathepsin S ligands. Additionally, we have shown that (MD) refinement further improves docking quality. The code of the Monte-Carlo minimization is freely available at https://bitbucket.org/abc-group/mcm-demo