Reservoir Permeability Prediction Based on Analogy and Machine Learning Methods: Field Cases in DLG Block of Jing’an Oilfield, China

AbstractReservoir permeability, generally determined by experimental or well testing methods, is an essential parameter in the oil and gas field development. In this paper, we present a novel analogy and machine learning method to predict reservoir permeability. Firstly, the core test and production data of other 24 blocks (analog blocks) are counted according to the DLG block (target block) of Jing’an Oilfield, and the permeability analogy parameters including porosity, shale content, reservoir thickness, oil saturation, liquid production, and production pressure difference are optimized by Pearson and principal component analysis. Then, the fuzzy matter element method is used to calculate the similarity between the target block and analog blocks. According to the similarity calculation results, reservoir permeability of DLG block is predicted by reservoir engineering method (the relationship between core permeability and porosity of QK-D7 in similar blocks) and machine learning method (random forest, gradient boosting decision tree, light gradient boosting machine, and categorical boosting). By comparing the prediction accuracy of the two methods through the evaluation index determination coefficient (R2) and root mean square error (RMSE), the CatBoost model has higher accuracy in predicting reservoir permeability, with R2 of 0.951 and RMSE of 0.139. Finally, the CatBoost model is selected to predict reservoir permeability of 121 oil wells in the DLG block. This work uses simple logging and production data to quickly and accurately predict reservoir permeability without coring and testing. At the same time, the prediction results are well applied to the formulation of DLG block development technology strategy, which provides a new idea for the application of machine learning to predict oilfield parameters

A human icam-1 antibody isolated by a function-first approach has potent macrophage-dependent antimyeloma activity in vivo.

We isolated a tumor B-cell-targeting antibody, BI-505, from a highly diversified human phage-antibody library, using a pioneering "function-first" approach involving screening for (1) specificity for a tumor B cell surface receptor, (2) induction of tumor programmed cell death, and (3) enhanced in vivo antitumor activity compared to currently used treatments. BI-505 bound to intercellular adhesion molecule-1, identifying a previously unrecognized role for this receptor as a therapeutic target in cancer. The BI-505 epitope was strongly expressed on the surface of multiple myeloma cells from both newly diagnosed and relapsed patients. BI-505 had potent macrophage-dependent antimyeloma activity and conferred enhanced survival compared to currently used treatments in advanced experimental models of multiple myeloma

Overexpression and involvement in migration by the metastasis-associated phosphatase PRL-3 in human myeloma cells

Multiple myeloma (MM) is characterized by accumulation and dissemination of malignant plasma cells (PCs) in the bone marrow (BM). Gene expression profiling of 2 MM cell lines (OH-2 and IH-1) indicated that expression of PRL-3, a metastasis-associated tyrosine phosphatase, was induced by several mitogenic cytokines. Cytokine-driven PRL-3 expression could be shown in several myeloma cell lines at both the mRNA and protein levels. There was significantly higher expression of the PRL-3 gene in PCs from patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering myeloma (SMM), and myeloma than in PCs from healthy persons. Among 7 MM subgroups identified by unsupervised hierarchical cluster analysis, PRL-3 gene expression was significantly higher in the 3 groups denoted as “proliferation,” “low bone disease,” and “MMSET/FGFR3.” PRL-3 protein was detected in 18 of 20 BM biopsies from patients with MM. Silencing of the PRL-3 gene by siRNA reduced cell migration in the MM cell line INA-6, but had no detectable effect on proliferation and cell-cycle phase distribution of the cells. In conclusion, PRL-3 is a gene product specifically expressed in malignant plasma cells and may have a role in migration of these cells

RARα2 expression is associated with disease progression and plays a crucial role in efficacy of ATRA treatment in myeloma

Specific genetic alterations in multiple myeloma (MM) may cause more aggressive diseases. Paired gene array analysis on 51 samples showed that retinoic acid (RA) receptor α (RARα) expression significantly increased at relapse compared with diagnosis. RARα encodes 2 major isoforms: RARα1 and RARα2. In this study, we examined the function of RARα2 in MM. Reverse transcription–polymerase chain reaction (RT-PCR) revealed ubiquitous RARα1 expression in MM cells, but RARα2 was expressed in 26 (32%) of 80 newly diagnosed patients and 10 (28%) of 36 MM cell lines. Patients with RARα2 expression had a significantly shorter overall survival on identical treatments. The presence of RARα2 remained significant on multivariate analysis. Knockdown of RARα2 but not RARα1 induced significant MM cell death and growth inhibition, and overexpressing RARα2 activated STAT3 and mitogen-activated protein kinase kinase (MEK)/extracellular signal–regulated kinase (ERK) signaling pathways. Interestingly, all-trans retinoic acid (ATRA) treatment induced potent cell death and growth inhibition in RARα2+ but not RARα2− MM cells; overexpressing RARα2 in RARα2-deficient MM cells restored sensitivity to ATRA. Furthermore, ATRA treatment significantly inhibited the growth of RARα2-overexpressing MM tumors in severe combined immunodeficiency (SCID) mouse model. These findings provide a rationale for RA-based therapy in aggressive RARα2+ MM