Turning Data into Knowledge: Data-Driven Surveillance and Optimization in Mature Fields

Abstract

Decision making in the oilfield is a crucial process in petroleum oilfield activities where numerous attributes and uncertainties exist in the complete process. In the development process of new fields, a well-organized historical database to minimize uncertainty is important as it decreases risks, provides better insight and robustness in decision making. Statistics is a strong tool to turn information or data into knowledge when used with care and physical understanding of the cause-effect relation between the attributes and the outcome. Unfortunately, historical data and learnings from the past cannot be used in an efficient way in oilfield decisions due to lack of systematically organized historical data where there is a huge potential of turning terrabytes of data into knowledge and understanding, for more successful decisions and results. On the other hand, using historical data on reservoir enginnering studies are generally very complex since it requires integration of vast amount data from several diciplines that have different data sources and from different scales, which is sometimes leading modelers to prefer new data collections from the field rather than using complex historical data on hand. In this sense, a multi-attribute based statistical model for each reservoir will greatly enhance the outcomes of future actions through bringing a statistical understanding to a physically more complex relation between the causes and effects or in other words, attributes and results. The data driven models are established for the desired phenomena by means of collecting relevant historical data, after which a multivariate regression is carried out to show the significance of each attribute in the model. These statistical models are then used to make future decisions in the same reservoir in such a way that attributes can be selected within the optimum ranges that yield best results and outcome. Attributes can consist of events and key parameters that influence the outcome. The model is illustrated to investigate the factor affecting the performance of vertical and horizontal wells in tight reservoirs. The data-driven model is validated with the numerical reservoir simulation model and used to determine the significance of each parameter and the optimum operating intervals.

Swab – Surge Pressure Investigation, and the Influence Factors, Prediction and Calculation (Review)

“…The strength of those pressure variations be contingent on the lowering the speed in other words, (tripping in) or retreating of the pipe out in other words, (tripping out). When the speed of the tripping is too high, the equivalent pressure variation is also high and will be higher than the formation fracture pressure [33]. High surge pressure reason for the formation fracturing, but high swab pressure leads to partial or in approximately cases full fluid losses, however for the worst-case situation well collapse may occur when the speed is very low, which will lead to a sluggish tripping operation, and that is reflected to the non-productive time (NPT) [34].…”

Modeling of Swab and Surge Pressures: A Survey

“…Pressure Generated Due to the Mud Flow in an Annulus: For the Bingham plastic fluid, the plasticity for an annulus is defined by Equation (44).…”

“…Bakshi [43] developed a unique nonlinear regression model to predict shale oil well performance, including optimal well sites and completion parameters. Temizel et al [44] investigated the factors that impact vertical and horizontal well performance in confined reservoirs.…”