Interaction of wells in flow production oil: a review of assessment methods and an analysis of their applicability

Introduction. The problem of identifying and assessing the mutual influence of wells is one of the key issues in the modern oil and gas industry and the least studied in relation to the conditions of gushing oil production. Forecasting well production volumes, planning well commissioning/decommissioning, and changing their operating modes is impossible without a clear understanding of these interactions.

Aim. The objective of this review is to analyze the applicability of existing models for assessing the interaction of well operating parameters under flowing oil production, based on published sources.

Methods. Articles for the review were selected according to the PRISMA methodology using keywords in the Google Scholar and Elibrary databases. Available full-text articles were manually screened for relevance to the research question.

Results. The review included 25 publications that matched the search query. Their analysis revealed a fairly wide range of applied methods and models: the hydrodynamic modeling method, the experimental method, the material balance model, the capacitance-resistance model (CRM), the two-dimensional filtration model, spatially autoregressive models (SAR, SEM, etc.), the spatiotemporal autoregressive moving average model (STARMA), the time series analysis (TSA) model, the vector autoregressive (VAR) model, and the Bayesian vector autoregressive (BVAR) model. Each of them has its own disadvantages, such as labor intensity, high cost, computational complexity, and sensitivity to gaps and outliers in the data. Many of them require certain assumptions and adaptations to assess the well-to-well relationships during flowing production. An analysis of the advantages and disadvantages led to the conclusion that the vector autoregressive model is the most promising for assessing the mutual influence of well oil flow rates during flowing production.

Conclusion. A wide range of well interference analysis methods allows for selecting a method that takes into account existing research limitations, but in all cases they require adaptation to flowing production.

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