A Critical Overview of Methodologies for Conducting Proper Production Lookback for Stimulation Treatments with Sparse Data

Abstract Well stimulation is the preferred practical approach for restoring and even increasing production in many oilfields, however, it becomes a challenging optimization opportunity for fields with a large number of wells and a high level of activity. Typical well stimulation treatments are chemical well cleaning (wellbore focused), acid stimulations (reservoir focused), cyclic steaming (targeting both reservoir and wellbore and are specific for heavy oil reservoirs) and hydraulic fracturing (targeting low permeability reservoirs). The success of the stimulation treatments is determined by technical lookbacks performed using production data. A lookback consists of contrasting the well production data prior to the treatment event, with the production realized after. The "down-oil" during treatment and the cost of the job are subtracted from the result to define the ultimate economic success. The challenge in conducting these production lookbacks is the availability of the production measurements. When a consistent and disciplined well testing program is executed in the field, production data is readily available for proper analysis. In contrast, when no production tests are measured, it becomes challenging to understand the success of the stimulation. This study outlines five approaches for conducting a proper production lookback in the case of sparse data and discusses the shortcomings and benefits through sensitivity analysis. The work uses synthetic data with behavior patterns inspired from the performance observed in wells from very mature oil reservoirs. The proposed research, as presented, is flexible in nature, and can be adapted to any type of stimulation treatments, as well as expanded to lookbacks on reactive work such as pump failures, sanding, etc. The authors believe that there is not "one" single approach to this complex task and encourage researchers to explore what is the best fit for their specific operations.