Iranian Journal of Oil and Gas Science and Technology

Abstract Rapid development of technologies, their increasing complexity and variety, together with limited organizational resources and efforts for survival in industrial competitions have made the task of appropriate technology selection a major challenge. The present research is aimed at the formulation of technology strategy related to oil production in one of the west Karoon oil fields in Iran. At the first, the processes and challenges of production in the studied oil field are recognized by the experts’ survey. Then, the priority of the challenges is evaluated and four key challenges of the considered field are recognized by using a paired comparison questionnaire and Chang Fuzzy AHP. In the next step, the existing and new technologies of oil production in the four recognized key challenges are determined. For each of the recognized technologies, the attractiveness assessment and capability assessment questionnaire are designed based on Jolly indexes and distribute in a sample composed of production engineering experts. Sampling is done by the non-random and purposive-judgmental method. Based on the results of the questionnaires, the attractiveness-capability matrix is designed by Morin’s model, and then based on the obtained technology portfolio, the strategies of each of the four areas are formulated and discussed.

Abstract Tedious calculations and simulations are needed to obtain an efficient production scenario and/or
proper field development strategy. Capacitance-resistance model (CRM) is proved to be a fast
reservoir simulation tool using just the field-available data of production and injection rates. This
approach sets a time-constant and a weighting factor (or well-pair connectivity parameter) between
each pair of injection and production wells according to their histories. In this study, we investigated
the behavior of the CRM parameters in synthetic reservoir models with different porosity and
permeability maps. Four reservoirs are considered with different porosities and permeabilities to study
their effects on CRM response. We defined a new parameter, named error to mean production ratio
(EMPR), to analyze the CRM performance. Some fluctuations are exerted on the production data to
evaluate the capability of CRM against variable production records. Porosity showed a stronger effect
on CRM parameters than the permeability based on the calculated EMPR. Unstable production
history would result in large error which can be corrected with some smoothing techniques on
variable production data. Also, a linear trend of EMPR was obtained with the change of porosity and
permeability or a combination of the two parameters within the reservoir.

Abstract Intelligent well technology has provided facility for real time production control through use of subsurface instrumentation. Early detection of water production allows for a prompt remedial action. Effective water control requires the appropriate performance of individual devices in wells on maintaining the equilibrium between water and oil production over the entire field life. However, there is still an incomplete understanding of using intelligent well concept to control unwanted fluids and the way this leads to improving hydrocarbon recovery. The present study proposes using intelligent well technology to develop a new integrated methodology for selecting/ranking the candidate wells/fields, interval control valve (ICV) size determination, and ICV setting optimization. Various technical and economical parameters weighted by expert opinions are used for candidate well/field ranking to implement the intelligent technology. A workflow is proposed for ICV size determination based on its effect on a predefined objective function. Inappropriate ICV size selection leads to suboptimum production scenarios. Furthermore, this study proposes an efficient ICV setting optimization in an intelligent well. The objective function can maximize cumulative oil, minimize water production, or conduct both. It was shown that for selecting the optimized cases, the balance between water and oil production under predefined criteria should be practiced. Real case studies were considered to demonstrate the effectiveness and robustness of the proposed methodology. A considerable improvement in the objective function was achieved using the developed methodology.

Abstract Reservoir models with many grid blocks suffer from long run time; it is hence important to deliberate a method to remedy this drawback. Usual upscaling methods are proved to fail to reproduce fine grid model behaviors in coarse grid models in well proximity. This is attributed to rapid pressure changes in the near-well region. Standard permeability upscaling methods are limited to systems with linear pressure changes; therefore, special near-well upscaling approaches based on the well index concept are proposed for these regions with non-linear pressure profile. No general rule is available to calculate the proper well index in different heterogeneity patterns and coarsening levels. In this paper, the available near-well upscaling methods are investigated for homogeneous and heterogeneous permeability models at different coarsening levels. It is observed that the existing well index methods have limited success in reproducing the well flow and pressure behavior of the reference fine grid models as the heterogeneity or coarsening level increases. Coarse-scale well indexes are determined such that fine and coarse scale results for pressure are in agreement. Both vertical and horizontal wells are investigated and, for the case of vertical homogeneous wells, a linear relationship between the default (Peaceman) well index and the true (matched) well index is obtained, which considerably reduces the error of the Peaceman well index. For the case of heterogeneous vertical wells, a multiplier remedies the error. Similar results are obtained for horizontal wells (both heterogeneous and homogeneous models).