Numerical Simulation of Temporal and Spatial Evolution of Stress Field in Unconsolidated Sandstone Oil Reservoir

Casing damage in the process of oilfield development is a serious problem, which is affected by geological structure, production technology and many other factors. To prevent casing damage, it is necessary to master the space-time evolution law of reservoir in-situ stress field, to provide support for casing damage prevention. Based on the perseage-stress coupling theory, taking the actual reservoir block as the research object, the change law of the in-situ stress field in unconsolidated sandstone reservoir is obtained through the fluid-solid coupling numerical simulation of the reservoir, and the internal correlation between the stress field and casing damage is analysed. The research results provide theoretical guidance for the formulation of casing damage prevention measures in the research block.

Casing Integrity Evaluation in Complex Subsurface Intervention Abandonment Wells Using Magnetic Ranging

This paper describes how active and passive magnetic ranging logging used while drilling subsurface intervention wells shows characteristics of the target well casing integrity and damage. Over the course of the development of a novel active magnetic ranging system and through several years of commercial application, data has been collected and analyzed to understand the characteristics of casing damage. This paper explains the methods used in field operations to collect this data. Using the gathered information, various stages of casing damage and poor integrity are shown. Results obtained from active and passive magnetic ranging are presented in the context of identifying casing damage. This is a departure from the standard methods of interpreting the data as it is not focused on locating a wellbore but determining the integrity of the casing. Casing integrity in idle wells is usually understood by conventional logging techniques until there is a restriction or damage on the well. Magnetic ranging logging performed during the intervention to abandon these wells can give an indication to operators of the casing integrity that otherwise would have been unknown without access to the damaged well. This can help optimize subsequent abandonment procedures as well as assist with field planning into the future to mitigate issues stemming from casing integrity and to identify the causes of previously unknown critical casing damage. The paper reports surface experimental data and compares it with two field examples. In the first field example, the passive magnetic interference from a hundred-year-old casing in the offset well caused more than 100000nT deviation from the reference field approximately 1ft away from the offset well, suggesting severe casing damage. The active magnetic signature measured simultaneously approaches zero, pointing to a lack of electrical continuity in the offset casing caused by a complete break. The second field example shows an offset well segment with passive interference of 7000nT in the presence of a stable active magnetic signal at approximately 2ft separation between wells due to possible casing damage without complete separation. The passive interference increases to 14000 nT at deeper depth while the active signal approaches zero due to a complete casing break. Novel application using the data collected by active and passive magnetic ranging techniques is being applied for the understanding of issues related to casing integrity.

Technology Focus: Completions (April 2021)

In the 2020 Completions Technology Focus, I stated that digitization will forever change how the most complex problems in our industry are solved. And, despite another severe downturn in the upstream industry, data science continues to provide solutions for complex unconventional well problems. Casing Damage Casing collapse is an ongoing problem and almost always occurs in the heel of the well. It prevents passage of frac plugs and milling tools. Forcing a frac plug through the collapsed section damages the plug, predisposing it to failure, which leads to more casing damage and poor stimulation. One team has developed a machine-learning (ML) model showing a positive correlation between zones with high fracturing gradients and collapsed casing. The objective is a predictive tool that enables a completion design that avoids these zones. Fracture-Driven Interactions (FDIs) Can Be Avoided in Real Time Pressurized fracturing fluids from one well can communicate with fractures in a nearby well or can intersect that well-bore. Such FDIs can occur while fracturing a child well and can negatively affect production in the parent well. FDIs are caused by well spacing, depletion, or completion design but, until recently, were not quickly diagnosed. Analytics and machine learning now are being used to analyze streaming data sets during a frac job to detect FDIs. A recently piloted detection system alerts the operator in real time, which enables avoidance of FDIs on the fly. Data Science Provides the Tools Analyzing casing damage and FDIs is a complex task involving large amounts of data already available or easily acquired. Tools such as ML perform the data analysis and enable decision making. Data science is enabling the unconventional “onion” to be peeled many layers at a time. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199967 - Artificial Intelligence for Real-Time Monitoring of Fracture-Driven Interactions and Simultaneous Completion Optimization by Hayley Stephenson, Baker Hughes, et al. SPE 201615 - Novel Completion Design To Bypass Damage and Increase Reservoir Contact: A Middle Magdalena, Central Colombian Case History by Rosana Polo, Oxy, et al. SPE 202966 - Well Completion Optimization in Canada Tight Gas Fields Using Ensemble Machine Learning by Lulu Liao, Sinopec, et al.

Bicentric Milling Approach Enables the Recovery of the Horizontal Section of an Unconventional Well in Turkey

During the completion phase of an unconventional well in Turkey, casing deformation represented a challenge to the operator and Coiled Tubing (CT) service provider due to the potential loss of almost 70% of the horizontal section. The deformation obstructed the path to continue the milling the remaining plugs. The implementation of bicentric mills and Multi-Cycling Circulation Valve (MCCV) incorporated in the milling assembly allowed efficient recovery of the horizontal section. The tubing condition analysis done by the engineering team showed that symmetric mills would not be beneficial. Conformance tubing was not an option. Bicentric milling approach was deemed the most viable solution. This approach consists of using offset mills where rotation causes the cutting head to cover an area larger than the mill's frontal face. However, this approach could lead the CT pipe getting stuck due to big junk left. The use of a MCCV, limiting the number of milled plugs, and performing a fishing run between milling runs were key to the success of the bicentric milling approach. The Turkish well was completed with ten stages isolated by nine aluminum plugs. During the fracturing of stage seven, an abnormal pressure drop was observed while keeping the same pump rate, indicating possible casing damage. After all the stages were fractured, the CT proceeded to mill the plugs using a 4.63-in Outside Diameter (OD) mill. After three plugs were milled, an obstruction was detected, indicated by frequent aggressive motor stalls at the same depth. A tapered mill was run to perform a tubing conformance, and after several hours of unsuccessful penetration, the tool was recovered. At the surface, the tool showed signs of wear around 4.268 in. A 4.0-in OD mill was used to drift this section, and it passed free. An analysis of both the plug anatomy and the casing condition was done to determine the most viable solution. A 4-in OD bicentric mill was designed to pass across the restriction with an adjusted eccentricity to allow higher contact area. Three bicentric milling runs were made with the limit of a maximum of two plugs per run to avoid a CT stuck situation due to the larger cuttings as a result of the mill's asymmetry. The sparsity of information on using bicentric mills for plug milling required research into unpublished practices for such scenarios. This paper documents bicentric milling approach, the use of offset mills, and the mitigation measurements taken during this project to avoid a stuck situation due to large debris generated.

High Resolution Acoustic Imaging of Plug Related Casing Damage in Hydraulically Fractured Horizontal Wells

While assessing post-hydraulic-fracture perforation growth using solid-state, high- resolution acoustic imaging tools, it was noted that plug failures were occurring at a high frequency. Though plug failures can be observed from hydraulic fracture surface pressure and flowrate data, the aggregate frequency, causes, and severity of the resulting erosional damage at plug locations was not previously well understood and highly speculative. The sub-millimetric three-dimensional imagery generated from high resolution solid-state acoustic tools significantly improved the industry's awareness of plug failure frequency, mechanisms of failure, and the resulting impact to stimulation efficiency. These acoustic tools helped to uncover the causes and explore possible solutions to failing plugs. This paper presents aggregate data encompassing casing wall loss at over 2700 plug locations and presents emerging trends that appear across the broader dataset. In addition, this paper showcases the usage of high-resolution acoustic imaging in two operator-specific case studies.

Prediction of Casing Damage: A Data-Driven, Machine Learning Approach

Casing damage is the result of a number of factors in the long process of oilfield development, so it must be correctly judged and repaired in time to ensure the normal production of the oil fields. With the development of data science, it has always been an imperative problem remained to be solved. In this paper, we adopt a data-driven and the machine learning approach to casing damage forecasts. Firstly, from the fields of geology, engineering and development, a lot of history data is collected and processed. Then, based on these dynamic and static data samples, the random forest algorithm is used to create the casing damage prediction model. Finally, after the model is tested in two fault blocks, the results indicate that accuracy rates are 91% and 75%, which proves the validity and performance of the mode.