Formation Pressure Inversion Method Based on Multisource Information

Summary The exploration and development of offshore oil and gas have greatly alleviated the tension of global oil and gas resources. However, the abnormal pressure of offshore reservoirs is more common compared with terrestrial oil and gas reservoirs, and the marine geological structure is complex, with the development of faults, fractures, and high and steep structures, which leads to the strong anisotropy of formation pore pressure distribution and uncertainty of pressure system change. In this paper, considering the corresponding characteristics of the randomness of the formation pressure prediction results in the Eaton equation for their respective variables, a formation pressure inversion method based on multisource information, such as predrilling data, bottomhole while drilling data, seabed measured data, and surface measured data, is established. On this basis, combined with the data of a well in the South China Sea, the variation law of the uncertainty of formation pressure prediction results under the conditions of predrilling data, measurement while drilling (MWD) data, and their mutual coupling is analyzed. The simulation results show that the uncertainty distribution of formation pressure prediction based solely on predrilling data shows linear accumulation trend with well depth, and the formation pressure inversion method based on multisource information can significantly curb the increasing trend of uncertainty when MWD data are introduced. Therefore, through the analysis of typical change patterns of monitoring parameters under normal/abnormal conditions during drilling, combined with the method of multisource information, the abnormal pressure information can be accurately predicted and inversed, which provides important support for wellbore pressure regulation under complex formation conditions.

Identification of Geo – Hazard Using Pore Pressure Analysis in ‘MAC’ Field, Niger Delta

Identification of geo-hazard zones using pore pressure analysis in ‘MAC’ field was carried out in this research. Suite of wireline logs from four wells and RFT pressure data from two wells were utilized. Lithologic identification was done using gamma ray log. Resistivity log was used to delineate hydrocarbon and non-hydrocarbon formations. Well log correlation helps to see the lateral continuity of the sands. Pore pressure prediction was done using integrated approaches. The general lithology identified is alternation of sand and shale units. The stratigraphy is typical of Agbada Formation. Three reservoirs delineated were laterally correlated. Crossplot of Vp against density (Rho) colour coded with depth revealed that disequilibrium compaction is the main overpressure generating mechanism in the field. Prediction of overpressure by normal compaction trend was generated and plot of interval transit time against depth show that there is normal compaction from 250m to about 1700 m on MAC-01, but at a depth of about 1800m, there was abnormal pressure build up that shows the onset of overpressure. A relatively normal compaction was observed on MAC-02 until a depth of about 2100m where overpressure was suspected. The prediction of formation pore pressure using Eaton’s and Bower’s method to determine the better of the two methods to adopt for pore pressure prediction shows that the pore pressure prediction using Eaton’s method gave a better result similar to the acquired pressure in the field. Hence Eaton’s method appears to be better suited for formation pore pressure estimation in ‘MAC’ field. The validation of the pore pressure analysis results with available acquired pressure data affirmed the confidence in the interpreted results for this study.

Machine Learning/Artificial Intelligence Driven Computer Vision for Cuttings Analysis Under Drilling and Completions

The shape and size of formation cuttings passing through a shaker screen can provide valuable insights about any potential downhole problems. Large size cuttings or carvings may indicate the presence of an abnormal pressure zone and hole size may be enlarged which may lead to NPT events (stuck pipe, loss circulation, etc.), asset loss or HSE incidents. We proposed a new method of real-time automated analysis of cuttings in the shale shaker enabling faster reaction to mitigate risks associated with drilling operations. The solution uses a camera on the shaker screen, capturing the cuttings images and applying computer vision and convolutional neural networks algorithms to identify and classify individual cuttings shape, size and type combined with wireline data to raise alarms on specific conditions and prescribe actions to mitigate the problem. The solution showed a remarkably high confidence in identifying the cutting types and size and in detecting potential problems at their early stage enabling the drilling engineers to take the corrective actions at the onset of an event.

Abnormal Pressure Distribution of Tertiary age formations in Middle & South Iraqi Oil Fields

Prediction of formation pore pressure gradient is a very important factor in designingdrilling well program and it help to avoid many problems during drilling operations such as lostcirculation, kick, blowout and other problems.In this study, abnormal formation pressure is classified into two types; abnormal highpressure (HP) and abnormal low pressure (LP), therefore any pressure that is either above orbelow the hydrostatic pressure is referred to as an abnormal formation pressure.This study concerns with abnormal formation pressure distribution and their effect ondrilling operations in middle & south Iraqi oil fields. Abnormal formation pressure maps aredrawn depending upon drilling evidence and problems.Three formations are considered as abnormal formations in the region of study, theseformations geologically existed in Tertiary age and they from shallower to deeper are: LowerFars, Dammam and Umm Er Radhuma, Formations. The maps of this study referred to eitherhigh formations pressure such as (Lower Fars and Umm Er Radhuma) or the low formationspressure such as (Dammam) in middle and south of Iraq. Finally these maps also suggested andshowed the area, where no field is drill until now, which may behave as high, low and normalformation pressure for every formation understudy.

Employing a Suite of Machine Learning Algorithms in a Holistic Approach to Trouble Stage Recognition and Failure Diagnostics

This paper explores a holistic approach to characterize trouble stages by applying automated event recognition of abnormal pressure increases and associating those events to formation and operational causes. This analysis of pressure increases provides insight into the potential causes of operational difficulties, and the related diagnostics can suggest improvements to future pump schedules. Improving how stages are pumped is profitable both in the short-term (reducing wasted fluid and chemicals, and other remediation measures) and in the long-term (increased well productivity). Quantifying how design decisions ultimately affect operations can help decrease the frequency of operational problems and help realize these gains. In this study, the identification of problematic frac stages was initially performed manually (stage-by-stage) using a cloud-based hydraulic fracture data application. During this process, the team recognized that the problem stages had their own characteristic pressure signature - a sudden unexplained pressure increase in the absence of rate changes. A machine learning algorithm was then developed to automatically identify this type of signature. Additionally, previously published machine learning algorithms were used to recognize other operational events of interest, e.g., when proppant reaches the perforations. Then by combining the various events and creating short search windows around each abnormal pressure increase, it is possible to find concurrent operations that may be associated with the observed pressure behavior. A subsequent statistical analysis revealed that abnormal pressure increases often coincided with changes in proppant concentration in problem stages (stages with abnormal treating pressure behavior). This behavior may be due to near-wellbore effects caused by the changing fluid flow dynamics. Furthermore, it was observed that treating pressures that behaved contrary to hydrostatic pressure effects may be useful in identifying when injectivity is lost and provide an early signal for screen outs. Through this holistic approach, we were able to identify trouble stages and discern some diagnostics for automated detection of abnormal treating pressure increases. The team was able to identify areas within the stages that were inefficiently pumped, resulting in cost-savings through optimization of proppant and friction reducer (FR) loadings while maintaining a level of caution to prevent screen outs. Finally, the automated detection of pressure anomalies offers a pathway to the real-time prediction and avoidance of operational difficulties such as pressure outs and screen outs.

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.

Experimental and Numerical Study of Air Vessel in Quasi-Two-Dimensional Transient Flow Analysis

An air vessel is a simple and effective device for protecting a pipeline system from abnormal pressure events. In order to design and select a suitable air vessel, analysis of the pipeline system is necessary. A quasi-2D approach can overcome some of the limitations of the conventional one-dimensional transient flow analysis method. However, only a few hydraulic components can be implemented in the quasi-2D approach. In this study, we propose a methodology for applying an air vessel in the quasi-2D model. An experimental study is performed for validating the proposed model. Although an identical approach involving an accumulator model is used to model the air vessel as in the 1D model, the quasi-2D model shows improved performance for the experimental system.