The diagnostic fracture injection test (DFIT) is widely used to obtain the fracture closure pressure, reservoir permeability, and reservoir pressure. Conventional methods for analyzing DFIT are based on the assumption that a vertical well is drilled in ultra-low permeability reservoirs with potential multiple closures but fails to consider horizontal wells. There is still significant debate about the rigorousness and validity of these techniques due to the complexity of the hydraulic fracture opening and closure process and assumptions of conventional fracture detection methods. The paper introduces a new method for detecting fracture closure pressure using the continuous wavelet transform (CWT). The new method aims to decompose the pressure fall-off signal into multiple levels with different frequencies using the CWT. This “short wavy” function is stretched or compressed and placed at many positions along the signal to be analyzed. The wavelet then convoluted the signal yielding a wavelet coefficient value. The signal energy is observed during the fracture closure process (pressure fall-off) and the fracture closure event is identified when the signal energy stabilizes to a minimum level. A predefined simple commercial fracture simulation case with known fracture closure, flow regime modeling, and actual field cases was used to validate the new methodology.

Hydraulic fracturing is frequently used to create enhanced wellbore connectivity to enable tight reservoirs to produce hydrocarbon. Many factors can be considered as risks to the success of fracturing operations. One of the risks arises in reservoirs that are close to a water-bearing zone. The risk of fracture growth into the water zone limits the stimulation options and eliminates the chances of using hydraulic fracturing treatment to improve well productivity, thereby restricting the well's future production and often resulting in lost recoverable reserves. In the Western Desert of Egypt, two wells were to be fracture stimulated with a risk of propagating into a nearby water zone. The productive pay of low-permeability reservoirs is separated from underlying water zones by a weak or no stress barrier. The proximity of the water zone to the hydrocarbon-producing zone varied from 20 to 40 ft, and containing the fracture height in such well conditions to prevent the fracture propagating into the underlying water zone becomes a serious challenge. This can jeopardize the post treatment well productivity. It therefore becomes necessary to prevent fracture height propagation from growing into the adjacent water zone. This case study presents a novel hydraulic fracturing technique, applied for the first time in Egypt's Western Desert that controls fracture height growth in the absence of in-situ stress contrasts. This technique places an artificial proppant barrier below the pay zone, close to the water-oil contact, creating high resistance to fluid movement and restricting pressure transmission, thus arresting unbridled vertical height growth of fractures. These barriers are created prior to themain fracture treatment by pumping heavy proppant slurry at fracturing rates carried in a fracturing fluid loaded with high breaker concentrations. The high breaker concentration breaks the gel fast, thus allowing the proppant to settle quickly to the bottom of the created fracture. The results from the application of this newly applied dual fracturing treatment technique have been overwhelming, with a 12-fold increase in production with no increase in water production. The application of this technique resulted in an increase in the net pressure at the end of main fracturing treatment indicating fracture containment within the zones of interest. The minifracture analysis, stress profile calculation, fracture geometry characterization, and no water breakthrough after the treatment support the fracturing design.

During the past few years, Khalda Petroleum company (KPC) are looking forward to significant steps toward improving the economic performance of hydrocarbon producing wells in the low-permeability, heterogeneous reservoirs through the application of high impact technologies used in unconventional wells for drilling, perforating, zonal isolation, fracturing and flowback that unquestionably helped improve well performance in an efficient and economical manner. Recently, the well architecture was changed from vertical completion profile to horizontal multistage fracturing, to increase the reservoir contact. This paper reviews and discuss the well completion and stimulation methods being implemented in horizontal wells fracture stimulation in Western Desert of Egypt allowing for multistages to be fractured in one continuous pumping operation including plug-n-perforation, cemented sliding sleeves with degradable isolation drop balls and Coiled tubing deployed abrasive jetting perforating on coiled tubing with annular path pumping of the fracturing treatment and sand plug isolation. This paper provides a comprehensive evaluation and comparison of these different techniques including an overview of these completion types, detailed engineering, post-stimulation flowback/clean out, discuss the benefits and considerations, and comparison of results from the multistage stimulation methods that were applied to improve the efficiency of multistage fracturing operations. Case histories are provided to support the obtained benefits and advantages, and lessons learnt are discussed along with recommendations and what to avoid in field operations. The case history will discourse the completion strategy, operational procedures, adeptness of the isolation and time frame used. On the other hand, operational setbacks encountered during the execution of the multistage fracturing treatment will also be encompassed in the paper; to allow for future improvement; and recommendations for future field operations to achieve faster fracturing and quicker production.

This study reviews methods for detecting fracture closure pressure in both unconventional and conventional reservoirs using mathematical models and fluid flow equations. It evaluates techniques such as the Nolte method, tangent method, and compliance method. The investigation relies on observing changes in fluid flow regimes from preclosure to postclosure using fluid flow equations to examine the postclosure flow regime effect on the G function. Reverse calculations model pressure decline across synthesized flow regimes, facilitating a detailed investigation of the closure process. The analysis reveals that the tangent method is sensitive to postclosure fluid flow, while the compliance method is less effective in reservoirs with significant tortuosity or natural fractures. This paper recommends assessing natural fractures’ characteristics and permeability to identify the source of leak-off before selecting a technique. It proposes integrating various methods to comprehensively understand subsurface formations, combining their strengths for accurate fracture closure identification and a better understanding of subsurface formations. The new proposed workflow employs the continuous wavelet transform (CWT) technique for fracture closure detection, avoiding physical model preassumptions or simplifications to confirm the results. This approach offers guidance on selecting appropriate methods by integrating different techniques.

Summary: Reservoir characterization is critical for successful oil and gas exploration, heavily reliant on detailed formation analysis from formation microimager (FMI) logs. However, interpreting these complex logs is time-consuming, subjective, and requires expert-level knowledge. This study addresses this challenge by proposing a novel approach that integrates computer vision (CV) and deep learning (DL) for automated and real-time interpretation of FMI logs. Our methodology leverages CV and DL techniques for automated feature extraction and classification from FMI data. A comprehensive training data set encompassing more than 2,500 images is utilized to train the DL model, enabling the identification of more than 50 distinct geological features, including bedding planes, fractures, and mineral variations. In addition, the study explores the creation of local stress maps from drilling-induced fractures to determine the present-day maximum horizontal stress (SHmax) orientation, crucial for optimizing wellbore stability and hydraulic fracturing strategies. This research presents a groundbreaking advancement in reservoir characterization through the synergy of automated FMI interpretation, CV, and DL. The developed model exhibits exceptional accuracy in geological feature classification, significantly surpassing traditional, human-centric interpretations. For instance, the model achieves a remarkable 92% accuracy in classifying ore than 50 geological features, demonstrably outperforming conventional methods. Furthermore, the developed model was applied to actual field cases to predict the stress field. The model was able to accurately predict the minimum horizontal stress (Shmin) and SHmax based on FMI logs, and the results were used to refine the geomechanical modeling and optimize hydraulic fracture orientation for enhanced hydrocarbon recovery. This work establishes a new benchmark for applying artificial intelligence in subsurface analysis, paving the way for future advancements in reservoir management and geomechanics. The implications are far-reaching, offering greater precision in geological interpretations, improved decision-making for production strategies, and ultimately, a more sustainable approach to hydrocarbon extraction. By automating tedious and subjective tasks, this approach not only reduces reliance on experts but also frees up valuable time for more strategic tasks. The ability to extract critical geological information with such accuracy from complex FMI logs translates to significant improvements in reservoir characterization and production efficiency.