Estimating the Properties of Naturally Fractured Reservoirs Using Rate Transient Decline Curve Analysis

Transient rate decline curve analysis for constant pressure production is presented in this pa- per for a naturally fractured reservoir. This approach is based on exponential and constant bottom-hole pressure solution. Based on this method, when In （flow rate） is plotted versus time, two straight lines are ob- tained which can be used for estimating different parameters of a naturally fractured reservoir. Parameters such as storage capacity ratio （co）, reservoir drainage area （A）, reservoir shape factor （CA）, fracture per- meability （ky）, interporosity flow parameter （,~） and the other parameters can be determined by this ap- proach. The equations are based on a model originally presented by Warren and Root and extended by Da Prat et al. and Mavor and Cinco-Ley. The proposed method has been developed to be used for naturally fractured reservoirs with different geometries. This method does not involve the use of any chart and by us- ing the pseudo steady state flow regime, the influence of wellbore storage on the value of the parameters ob- tained from this technique is negligible. In this technique, all the parameters can be obtained directly while in conventional approaches like type curve matching method, parameters such as co and g should be ob- tained by other methods like build-up test analysis and this is one of the most important advantages of this method that could save time during reservoir analyses. Different simulated and field examples were used for testing the proposed technique. Comparison between the obtained results by this approach and the results of type curve matching method shows a high performance of decline curves in well testing.

Integrated lipidomics and proteomics network analysis highlights lipid and immunity pathways associated with Alzheimer's disease

Background:There is an urgent need to understand the pathways and processes underlying Alzheimer's disease(AD)for early diagnosis and development of effective treatments.This study was aimed to investigate Alzheimer's dementia using an unsupervised lipid,protein and gene multi-omics integrative approach.Methods:A lipidomics dataset comprising 185 AD patients,40 mild cognitive impairment(MCI)individuals and 185 controls,and two proteomics datasets(295 AD,159 MCI and 197 controls)were used for weighted gene CO-expression network analyses(WGCNA).Correlations of modules created within each modality with clinical AD diagnosis,brain atrophy measures and disease progression,as well as their correlations with each other,were analyzed.Gene ontology enrichment analysis was employed to examine the biological processes and molecular and cellular functions of protein modules associated with AD phenotypes.Lipid species were annotated in the lipid modules associated with AD phenotypes.The associations between established AD risk loci and the lipid/protein modules that showed high correlation with AD phenotypes were also explored.Results:Five of the 20 identified lipid modules and five of the 17 identified protein modules were correlated with clinical AD diagnosis,brain atrophy measures and disease progression.The lipid modules comprising phospholipids,triglycerides,sphingolipids and cholesterol esters were correlated with AD risk loci involved in immune response and lipid metabolism.The five protein modules involved in positive regulation of cytokine production,neutrophil-mediated immunity,and humoral immune responses were correlated with AD risk loci involved in immune and complement systems and in lipid metabolism(the APOE ε4 genotype).Conclusions:Modules of tightly regulated lipids and proteins,drivers in lipid homeostasis and innate immunity,are strongly associated with AD phenotypes.