Analyzing Well Events To Increase Oil Recovery
Authors: Bailey R. , Lu Z. , Shirzadi S. , Ziegel E. 
Published: February 2015

Technology Update

The human mind, presented with a series of time data, will generally look for common features, label them as events, and seek correlations against other timelines. This approach is fallible because questionable data might be selected in attempting to find supporting evidence for a perceived pattern. A better alternative would be to establish an automated, comprehensive, dispassionate, independent, and statistically valid process for pattern matching. It is a major challenge to develop such a process. But if all the objectives are met, then many evaluations can be carried out using an “event-based” analysis.

Instrumented systems and the analysis of their time-series data can provide a range of events and channels through which those events can interact. Some influences will be automatically transmitted by closed-loop process control schemes. Others will be prescribed by written operating practices, and still others will be the result of “custom and practice” on the part of operators, supervisors, and planners.

Despite the presence of these systematic, nonreservoir-derived reasons for patterns of change, the historical daily production data is the most direct measurement of the hydrocarbon fluids being extracted from the reservoir. The business case to pursue increases in efficiency of extraction and total recovery, while simultaneously being able to accurately assess the effectiveness of measures taken to support, sustain, and maximize that production, is clear.

New ‘Top-Down’ Analysis

Understanding the flow from each well as a result of an agreed allocation process, based on the assessment of flow data from the well’s tests, allows history matching. Typically, one or more reservoir simulation models are tuned to the observed flow and pressure data. These processes represent a “bottom-up” approach to understanding reservoir performance, through which a development plan can be monitored and updated as necessary.

However, BP has been working on a separate but entirely complementary “top-down” approach in which operational data is analyzed without preconceptions about the reservoir structure. This approach allows the rapid formulation of a workable “model,” thus avoiding the need for an involved history-matching procedure. The top-down approach uses the capacitance resistivity model (CRM) combined with an analysis of the operational time-series data based on “events” in that data.

Event-Based Approach

Firstly, one must choose the production and injection wells to include for the asset being studied. These will be sufficiently instrumented to provide a time-series data stream containing events, which are distinguishable from the unavoidable sources of measurement noise. The wells will be material to the oil recovery plan and will include some key injection wells, in addition to producers in which output might be enhanced by the injectors.

Next, one must choose a period for analysis. Recent data is likely to be the most instructive in terms of potentially assessing probable future events, but better data-quality periods may exist. Quality may be measured in terms of working instrumentation, constant or stable numbers of injection and production wells in service, and the absence of changes in operating regime, such as artificial lift or the breakthrough of water or gas into production wells.

Then, one must choose the well attribute that will be used as the variable to indicate the occurrence of an event. Typically, the choice will be an allocated (or possibly measured) flow, an aspect of production, such as water cut or gas/oil ratio, or a direct intensive measurement, such as a pressure or a temperature. Similarly, injection wells will have some (typically) corresponding attribute, such as injection rate or pressure.

The event-based analysis begins with the marking of events for each production and injection well. Parameters are used to control the relative occurrence of events. Some iteration is typically required until a set of events that make sense organically and pass visual assessments has been achieved. Visualizations made using the association software assist with events evaluation.