No abstract

Streamline-based methods provide an effective model-based tool for assessing flow patterns and well allocation factors. This type of diagnostic information is a key enabler in model-based waterflood management. Alternatively, data-driven techniques are available for waterflood diagnostics and estimating injector/producer connectivity, including one based on the Capacitance-Resistivity Model (CRM). The purpose of this paper is to compare the results of these two complementary approaches using a sector area representative of a mature field. In the first part of the paper, a workflow which allows a comparison of the streamlines and the CRM methodologies, regardless of the uncertainty of the simulation model, is introduced. The limitations of both approaches are discussed. In the second part of the paper, the CRM data-driven approach is used to determine the statistically significant allocation factors for the injection and production data from the sector reservoir model, and they are compared with the streamline-based factors. The results and guidance for the integration of the two approaches are presented.

No abstract

Summary Under currently known reservoir drive mechanisms, initial exploitation of a new field usually leads to drilling in several regions of the field for optimum production. As the field matures, it often is necessary to drill new wells to recover oil reserves trapped because of undetermined reservoir heterogeneity and not acknowledged during initial field development. Targeting new wells in a mature field is challenging because it calls for consensus among interdisciplinary groups (reservoir and production engineers, geoscientists, and drilling and facility engineers). The challenges usually involve estimating the cost and production of the new wells and finding convincing arguments that the existing wells will not drain the trapped reserves adequately. This paper provides a team approach to confront these challenges to target new wells in the mature Prudhoe Bay field. The field example shows that additional oil recovery was obtained with infill drilling. Introduction The large, unrecovered mobile oil resource base in the Prudhoe Bay field provides an immediate objective for additional recovery. A significant amount of oil was bypassed at the initial stage of field development because of a lack of knowledge of geologic information and reservoir heterogeneity (defined as the product of depositional and diagenetic processes and structural complexities). Therefore, as part of field depletion planning and to mitigate field decline, infill drilling of new wells often is proposed to increase recovery. In a large field, such as Prudhoe Bay, drilling new wells calls for interaction among professionals in several disciplines: reservoir, facility, environmental, production and drilling engineers, geoscientists, and petrophysicists. In addition, the field is divided into two operating areas. Therefore, cooperation with the partners also is required. As in other infill-drilling projects, the development plan for drilling new wells in the Prudhoe Bay field was addressed with a multidisciplinary team approach. Also, as in other optimization strategies reported in the literature, the team approach focused on targeting new wells to optimize the depletion plan.

Manual inspection data are often collected by an operator for its non-piggable pipelines to determine the state of the pipeline infrastructure across its portfolio of upstream operating locations. This inspection effort confirms the current understanding of corrosion mechanisms and the success of ongoing protective measures. With sufficient data for a pipeline, statistical estimation of the extreme wall loss value and the percentage of the pipeline that would have wall thickness less than designated important thresholds can be undertaken. The principle difficulty in the process for estimating a worst case for corrosion is validating an appropriate statistical distribution for use in the computations. A database of wall loss measurements from in-line inspection (ILI) provided the basis for a clever data-based solution. Statistical sampling and estimation were used to create datasets for which a number of different distributions could be evaluated. The results of this exercise were used to build a statistical distribution selection algorithm that was calibrated versus statistics that could be calculated from the available manual inspection data for any pipeline. This process for determining the expected worst corrosion for a pipeline is quick and automatic. It allows the assets to use their inspection data to easily update their understanding of pipeline condition as new inspections become available. This guides decisions for further inspection needs or possible repair requirements. The process is readily updated as new ILI's provide additional information that can be used to improve the extreme value distribution selection algorithm. This paper builds on technology that is described in SPE 128697 (Ziegel et al 2009), which demonstrated that the adequacy of the number of inspections for a pipeline could be assessed by using the available ILI data to establish sampling distributions as the basis for sample size requirements for manual inspections. The two technologies together turn a lot of pipeline inspections into a direct awareness of the statistical assuredness of the current understanding of pipeline condition. The additional information improves the ability of inspection and repair functions to ensure that oil remains in pipelines. Loss of containment can result in millions of dollars of lost profits in a single day and have a very negative impact on the reputation of the operator.