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Abstract PI (Productivity Index) degradation is a common issue in many oil fields. To obtain a highly reliable production forecast, it is critical to include well and completion performance in the analysis. A new workflow is developed to assess and incorporate the damage mechanisms at the wellbore, fracture and reservoir into production forecasting. Currently, most reservoir models use a skin factor to represent the combined well damages mechanisms. The skin factor is adjusted based on the user's experience or data analysis instead of physical modeling. In this workflow, a detailed model is built to explicitly simulate the damage mechanisms, assess the dynamic performance of the well and completion with depletion, and generate a physics-based proxy function for reservoir modeling. The new workflow closes the modeling gap in production forecasting and provides insights into which damage mechanisms impact PI degradation. In the workflow, a detailed model is built, which includes an explicit wellbore, an explicit fracture and the reservoir. Subsurface rock and flow damage mechanisms are represented explicitly in the model. Running the model with an optimization tool, the damage mechanisms’ impact on productivity can be assessed separately or in a combination. A physics-based proxy is generated linking the change in productivity to typical well parameters such as cumulative production, drainage region depletion and drawdown. This proxy is then incorporated into a standard reservoir simulator through the utilization of scripts linking the PI evolution of the well to the typical well parameters stated above. The workflow increases the reliability of generated production forecasts by incorporating the best representation of the near wellbore flow patterns. By varying the damage mechanism inputs the workflow is capable of history matching and forecasting the observed field behavior. The workflow has been validated for a high permeability, over pressured deep-water reservoir. The history match, PI prediction and damage mechanism analysis are presented in this paper. The new workflow can help assets to: (1) history match and forecast well performance under varying operating conditions; (2) identify the key damage mechanisms which allows for potential mitigation and remediation solutions and; (3) set operational limits that reduce the likelihood of future PI degradation and maintain current performance.
Abstract The objective of this work is to assess the impact on productivity decline of altering the completion type in a deepwater Miocene reservoir. Typically to date, these types of assets have utilized Cased Hole FracPack (CHFP) completions as a basis of design. Wells in the Gulf of Mexico targeting the deepwater Miocene plays have seen significant Productivity Index (PI) decline within the first few years of production. Open Hole Gravel Pack (OHGP) and Open Hole FracPack (OHFP) completion types were selected as potential alternatives to CHFP. A coupled well, reservoir and geomechanical model was created to assess the impact of multiple potential damage components on matching the observed inflow performance from production logs. The model assesses probabilistically the weighting of each of six damage mechanisms (creep, fracture conductivity, fines migration, fracture connectivity, off-plane perforation contribution and drilling/completion fluid damage) on well performance. Based on this weighting, an assessment can then be made of their impact on the alternate completion types. Previous studies (Knobles et al. 2017) have indicated that cased hole completions are particularly susceptible to PI decline. Specifically, when unpropped perforation tunnels collapse, they reduce the inflow area into the wellbore and create a flow restriction. In higher permeability formations, the perforations not connected to the fracture (i.e. off-plane perforations) can contribute a significant portion of the well's production. It is important to note that if the connectivity and packing of the perforations is optimized and fracture is placed to within design specifications, little PI decline is observed. However, in the real world, this is not always the case. Three wells were used in this analysis. Two wells where decline was observed and a third well where no significant decline was observed. Results from the study indicated that if the two underperforming wells had utilized an OHGP completion, the PI degradation would have been mitigated. However, the upside production seen from the third well would not be attainable had the well been completed as an OHGP on an equivalent well trajectory. The results of the study also indicated that minimizing the drilling damage would be integral to the success of the OHGP completion in comparison to optimizing the completion placement in a CHFP. The paper addresses a significant issue of PI decline affecting deepwater wells and presents a potential remediation technique based on alternate completion types. The paper also presents a new methodology based on Design of Experiment to assess the contribution of various damage mechanism while incorporating the uncertainty around each based on available measurements.
Well productivity reduction over time is one of the critical issues for deep-water wells with huge implications on expected recoveries from these wells. It is important to account for this uncertainty accurately in order to generate reliable production forecasts. Typically, reservoir simulation engineers utilize an expression (e.g. linear or exponential) for modeling changes in skin or Productivity Index (PI) over time as a function of either time or cumulative liquid production or pressure depletion. These commonly used single variable-based PI degradation modeling methods are easy to implement with a flow simulator, but they do not address the multi-dimensional nature of PI degradation which is a result of multiple subsurface effects combined with operational conditions. As a result, these single variable-based modeling methods generally do not have good reliability for predicting PI degradation trend.
This article proposes a method for predicting reduction in producing well's PI by integrating a few key well operating variables (drawdown, borehole depletion, and water cut) into a single mathematical formulation. One of the important assumptions of IPDM is that certain critical drawdown pressure exists for each well in the field. When a well is operated below the critical drawdown pressure, negligible to no PI reduction is observed; however, when drawdown exceeds the critical drawdown value, noticeable PI reduction is seen. The reduction level depends on the ratio of current drawdown pressure to critical drawdown pressure, as well as on pressure depletion and water cut levels. By doing so, the global trend and local granularity of PI reduction are well captured. The concept of critical drawdown used in IPDM is aligned well with the awareness of safe drawdown among Reservoir Management practitioners. Critical drawdown ranges derived from IPDM form a useful analog data set while deciding safe drawdown limits or forecasting PI degradation trends for future wells.
The predictability and generality of IPDM were assessed with historical well test (or PTA – Pressure Transient Analysis) data from more than 50 wells across seven deep-water fields. A detailed workflow of implementing IPDM was demonstrated through one of the field applications. From practical implementation point of view, IPDM method can be easily written in Excel Macro or using a PYTHON script or any other programming language. Once the methodology is coded (or implemented), it can then be plugged in a dynamic flow simulator (e.g. INTERSECT™). A similar approach is usually taken while incorporating single variable-based method for forecasting PI degradation into a flow simulator. Compared to the coupled modeling of geo-mechanical simulator - dynamic flow simulator, the IPDM is a much simpler approach to use and also enables engineers to evaluate the impact of key operating conditions on individual well's PI.
Abstract Long term productivity is impacted by both the magnitude and rate of skin growth during the life of the well. Many deepwater Gulf of Mexico (GOM) Operators experience premature Productivity Index (PI) decline, which significantly impairs the economics of many major capital projects (MCP). Stringent application of best practices in both design and execution phases of deepwater wells can alleviate premature skin growth during the well life. The current study summarizes some critical best practices that impact the rate of PI decline in a broad dataset of recent cased hole frac pack (CHFP) completions. This study outlines a methodology to assess the relative impact of numerous variables that affect the frac pack deliverability. Application of this methodology requires early project phase development of a 3D mechanical earth model optimizing the number and location of drill centers and associated well paths ensuring they are optimal for frac pack completions. The methodology includes the completion phase, and that the assessment of completion-execution performance is fed back into the planning of future wells. This feedback loop across deepwater projects identifies best practices in execution and continual improvement in future completions. Over 70 CHFP completions in six different deepwater fields are assessed and correlated to their productivity trends. Information from this broad dataset helps to develop new and confirm established best practices. These best practices are derived by cross-functional analysis of factors related to the reservoir, completion-design, execution and the effects on long term deliverability of these wells. Our analysis concluded that three specific factors showed the highest impact in achieving a successful CHFP with improved initial skins and anticipated lower rate of skin-increase with reservoir pressure decline. While many sub-factors contribute to their relative impact, these three key factors include: 1) fracture- wellbore connectivity; 2) sufficient fracture-width and conductivity in the near-wellbore region to withstand changing reservoir conditions; and 3) an undamaged and intact annular proppant pack. The details associated with improving the likelihood of achieving each of the key factors and other findings are explored in-depth in the current work. Consideration of these high-impact variables and other best practices is-assessed and quantified within the new workflow, providing feedback to improve future completions and MCP developments. Our data set provides the most comprehensive collective study of frac pack completions in the Gulf of Mexico. Furthermore, the cross-functional expertise that contributed to the analyses of sub-variables brought the "best minds to the table". These attributes and the wide number of variables that were examined outline key best practices that should apply to any CHFP execution. The improved completions- workflow and comparison between producing CHFP completions allow prediction of future productivity trends. Possession of this knowledge enhances the predictability of production forecasting for business planning purposes.
Tan, Yunhui (Chevron Energy Technology Co.) | Li, Yan (Chevron Energy Technology Co.) | Rijken, Margaretha C. M. (Chevron Energy Technology Co.) | Zaki, Karim (Chevron Energy Technology Co.) | Wang, Bin (Chevron Energy Technology Co.) | Wu, Ruiting (Chevron Energy Technology Co.) | Karazincir, Oya (Chevron Energy Technology Co.) | Williams, Wade (Chevron Energy Technology Co.)
Many deepwater wells experience steep productivity declines. Some field observations indicate that this decline is partly attributable to fines-migration effects. In this paper, we present a numerical workflow to simulate the effect (over time) of flow-induced fines migration on production decline in deepwater reservoirs. A permeability-reduction function was extracted from long-term coreflood tests and implemented into a reservoir simulator. Using the permeability-reduction function, production degradation caused by fines migration was simulated in a detailed single-well model. From previous research, it was understood that fines migration does not start until the flow velocity is greater than the critical velocity. After many long-term coreflood tests, or extended fines-migration (EFM) tests, we concluded that the permeability damage induced by fines migration is a function of the pore-volume (PV) throughput (fluid volume flowing through the core divided by the PV of the core). To address these observations, the numerical model was updated such that the interstitial flow velocity was tracked in each individual cell. When the interstitial velocity is greater than the critical velocity, the cell’s permeability follows the permeability-reduction trend obtained from laboratory data. Validation of the workflow is performed using a cylinder model to match the laboratory test core-plug data. A detailed 3D model was constructed to study the fines-migration effect in each part of the near-wellbore (e.g., perforation, fracture) region and the reservoir. As expected, fines migration started near the perforation where the flow velocity was the highest. Depending on the permeability-decline rate, the production asymptotes eventually reached a constant value after a certain period. Both the decline rate and the ultimate residual permeability had a strong effect on the final production. Sensitivities were run to study the effect of fines migration in different completions. To the authors’ understanding, this is the first time that laboratory-based fines-migration data were incorporated into a reservoir simulator to predict the production decline using experiment-based fines-migration functions. This workflow will help reservoir engineers predict the damage caused by fines migration, predict production decline, and plan for remediation.