bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Impact of Poor Logging Data on Mud Weight Prediction Through Development of Approximate UCS- Based Correlations for Fracture and Stability Pressure Gradients
Authors: Al Zeer S. , Hossain M. M.
Published: February 2024

This proposed paper aims to evaluate the impact of poor logging data on the prediction of accurate mud weight and to assess the impact of Uni-Axial Compressive Strength (UCS) for weak to moderate and moderate to strong formations with fracture and minimum mud/stability pressure gradients. Six approximate correlations are developed as a function of UCS to predict the fracture and stability pressure gradients for the stated range of formation.

This study employed MATLAB code for the analysis of logging data to identify instabilities in the offset well and estimate the rock's mechanical properties through numerical simulation. By incorporating data from nearby wells, the logging data were rectified, facilitating the recalculation of optimal mud weights using a geomechanical earth model. The case study focused on three UCS correlations suitable for weak to moderate and moderate to strong formations. Scatter plots of fracture and stability pressure gradients against UCS obtained from MATLAB simulation were generated and plotted, which were then combined to derive approximate correlations for each correlation through regression analysis for the prediction of minimum and maximum mud weights (referred to as stability and fracture pressure gradient) as functions of UCS within the considered formation range.

Inaccurate logging data, especially caused by hole enlargements or breakout for offset wells can lead to erroneous estimations of recommended mud weights for drilling plans which can pose serious technical, safety and financial risks in drilling operations. The proposed approach of corrections to the offset well's data has been observed to provide more reliable and safer recommended mud weights. About the UCS exanimation, the first correlation is a function of static Young’s modulus, the second is a function of P-velocity, and the third is a function of dynamic Young’s modulus and dynamic Poisson’s ratio. The first and second correlations have shown the representation of moderate to very strong formations which agrees with the formation of the case study while the third correlation has produced values of UCS which are not representative of the well as it demonstrates the behavior of weak formations. Regarding the relationship between UCS and mud weights, it was found that as UCS increases, the fracture pressure gradient increases while the stability pressure gradient decreases for all three correlations.

The workflow presented in this paper can improve the estimation of optimal mud weights for new wells before drilling, potentially reducing uncertainties and the associated risks of wellbore instability. The approximate correlations for various formation types, also detailed in this paper can potentially serve as approximate as well as valuable engineering tools that enhance the estimation of optimal mud weights without the need for in-situ stress data and other mechanical properties of the formation.
First Trial of On-Demand Activated Axial Agitation System on Abu Dhabi Offshore Operation
Authors: Wahyudi A , Biyanni H , Tamuli A , Agarwal C , Hermawirawan L , Nowfal M , Che R
Published: February 2024

The Axial Agitation System is an effective friction reduction solution on directional motor and rotary steerable system (RSS) bottomhole assemblies (BHA). Drilling performance in a high dynamic friction environment is often limited due to high fluctuations in the drillstring’s revolution per minute (RPM) and torque, often coupled with stick/slip. This drilling dysfunction diminishes the amount of energy spent on rock shearing. The weight on bit and torque from the surface are not efficiently transmitted to the bit. If left unchecked, the rate of penetration (ROP) will be suboptimal, and stick/slip may lead to mode-coupling of vibration (lateral, axial) and BHA whirl, increasing the risk of downhole tool failures and non-productive time (NPT).

The Axial Agitation System has been successfully paired with RSS drilling BHAs in ADNOC Offshore fields in 13 different wells. The first three wells were qualitatively and quantitatively studied by Gohel et al.6. Performance analysis performed on each well against a comparable offset well consistently demonstrates considerable improvements in drilling performance. While efforts have been made to select offset wells with minimal differences (well trajectory, BHA and drillstring design, formations, etc.), there is still a variety of RSS applications where the Axial Agitation System’s benefits need to be fully quantified and objectively concluded.

An On-demand Axial Agitation System that can be activated—when needed—allows for a more objective comparison since the bit, BHA, drillstring components, and dynamic environment remain constant. This paper analyzes the first trial results of this on-demand axial agitation system by benchmarking the drilling performance before and after activation in two wells offshore Abu Dhabi.

The data were acquired from two wells with different directional profiles and BHAs to prove that the agitation effect was equally tangible in different applications. The analysis metrics include, but are not limited to, mathematically modeled algorithms during the planning stage, surface data acquisition systems, and downhole dynamics measurements.

The results of the analysis quantitatively indicate an enhancement of the drilling dynamics by more efficient use of available energy for maintaining and improving the ROP. To further enhance system performance, lessons learned are identified and discussed in this paper.
Reducing the Sand Production from Semi-Consolidated Sandstone Formation by Mitigating the Silica Dissolution Factor During Water Breakthrough
Authors: Alameen Mohammed B. , Elraies Khaled A. , Mohyaldinn Mysara , Almansour Abdullah , Gmail Abdallah M. , Hagar Haithm
Published: February 2024

Sand production is a common problem associated with semi-consolidated sandstone due to the reaction between the injected water and sand formation. In this paper, the mechanism of quartz hydrolysis during water breakthrough is investigated to establish a relation between silica dissolution and rock strength as a function of water saturation. The study is conducted with static and dynamic experimental approaches to investigate the relationship between sand (quartz) and water, which is called quartz hydrolysis. The static approach used pure quartz fine and coarse particles at different water compositions and temperatures to find the reaction between silica and water. From static results, some cases have been selected to examine further through a dynamic approach (core flooding test) as a function of the water saturation. Then using a UV-Vis spectrophotometer to measure the silica concentration and dissolution rate. Whereas unconfined compressive strength is conducted to measure the change in sandstone core strength. The results regarding static conditions show that Distilled water has a higher impact on silica dissolution than the brine base. This is related to the saturation level of the solution reached. In terms of the pH effect, it has been found that the highest amount of silica dissolution is 12 pH then 3 pH. Meaning that at the highest pH 12 and lowest pH 3 the silica dissolution increases sharply. This is related to OH and H ions present in both high and low pH stimulating the reaction. Regarding salinity effect, it is seen that Sodium chloride and potassium chloride have the highest effect in silica dissolution. While for the core flooding test, the results show that an increase in water saturation leads to reduce the core strength as a function of an increase in silica dissolution, which weakens the grain particles and connections at grain-to-grain contact, ultimately reducing core strength and causing sand production. It is concluded that water quality has a significant impact on the amount of sand dissolved, and a correlation between silica dissolution and rock strength as a function of water saturation is established. This study’s findings are useful in designing optimum water quality for water injection and mitigating sand production issues in sandstone formations. Also, understanding the main reasons behind sand production for the semi-consolidated sandstone formation.
4D Geomechanics New Modelling Approach using Machine Learning Predictions for Giant North Kuwait Jurassic Field
Authors: Haas M. , Al-Hamad N. , Heiland J. , Hussein A. M. , Shekhar S. , Srivastava D. N. , Ramon J. , Abdel-Basset SLB M. , Al-Anzi Abdullah , Al-Ajmi Moudi
Published: February 2024

Drilling new wells in the deep Jurassic formations of the North Kuwait fields has gained increased importance given its high gas production potential, which represents a strategic target for Kuwait's domestic gas independency. However, reaching these formations bears severe drilling risks and challenges caused by differentially depleted reservoir sections that lead to arbitrary stress distributions and high pore pressure zones. This study presents an integrated 4D geomechanical model that includes a machine learning model for the development of a drilling expert system. This allows to predict drilling events along any arbitrary well trajectory in the 3D field model by analysing the stress field based on the construction of a mechanical earth model. The model further assesses completion quality to hydraulically fracture the rock mass. The simulation covers a 34 million grid cell model and is based on high-resolution geological information, seismic inversion data, rock core laboratory data featuring uniaxial, triaxial, multiaxial geomechanical tests, scratch, creep behaviour and thick-wall cylinder tests as well as petrophysical logs from 54 deep Jurassic wells. Seismic acoustic impedance data served as a proxy for co-kriging the spatial distribution of geomechanical properties. In addition, fault and fracture models were incorporated at reservoir levels.

The drilling expert system allows for an easily applicable and efficient extraction of the required drilling parameters and rock properties along any arbitrary well trajectory to conclude on the probability of drilling events occurring. The derived wellbore stability analyses reduce the costly and time-consuming risks of wellbore collapses when passing through high pressure formations. The simulated 4D stress field yields a strike-slip stress regime with a minimum horizontal stress direction of 135°. Identified zones in the reservoir sections show high hydraulic fracture potential given by the under- and overlying stress barriers in the anhydrite formations for stress containment. The evaluation of critically stressed, natural fractures as well as faults lead to the conclusion of potential reactivation due to high pressure injections in the Najmah reservoir for hydraulic fracturing. The study successfully showcased the integration of domain geomechanics expertise and data science, to derive a 4D geomechanical earth model as input for training machine learning models to evaluate drilling integrity and hydraulic fracturing potential.
Research and Application of Rock Typing Using Deep Learning in Prediction of Carbonate Reservoirs of H Oilfield, Iraq
Authors: Xiaoli Gao , Lu Lv , Bo Wang , Yongli Wang , Ailing Wang , Junhui Hu
Published: February 2024

Objective/Scope

The loss of some important logging data especially lithology data in old oilfields and huge cost in manpower and material on drilling and coring have brought great difficulties to the development of oilfields. A new method using deep learning for rock typing is achieved to classify and count the limited logging data, establish appropriate pore-permeability (PP) relationship and reduce the risk of reservoir prediction, which provides a concise and effective way for carbonate rock prediction.

Methods, Procedures, Process

In allusion to the existed problems, the paper collects, ranks the correlation between rock types and conventional logging data, which establishes a neural network model based on deep learning, divides the carbonate reservoirs into 4 types, and estimates the pore-permeability relationship for each type. Finally, a pore-permeability cloud simulation was performed based on the geo-statistical inversion to set up a high-precision reservoir static model with perfect well-seismic tie. The reliable permeability property can be obtained which helps to accurately depict the spatial distribution of the reservoirs.

Results, Observations, Conclusions

The carbonate reservoir of M formation for H oilfield in the Middle East is of complex pore structure with strong heterogeneity and poor relationship of the pore-permeability (PP). The logs DT, GR, Density, Porosity as the input features of deep learning is optimized to train neural network models, which are applied to the sample for testing and verification. The sample tests from the optimized neural network model are as accurate as 86.8%. The results show that rock typing using deep learning and well logs found a non-linear mapping relationship which effectively and reasonably divided the carbonate reservoirs into 4 types. The geological statistics and stochastic simulation in geo-statistical inversion organically combine seismic information with rich reservoir parameters such as porosity, rock typing, permeability and so forth. The permeability inversion result is highly consistent with the drilling data, which means the reservoir distribution patterns and regularity have been greatly improved, the local characteristics of the reservoir have been described more detailed and accurate.

Novel/Additive Information

The paper establishes and optimizes a neural network model based on deep learning which extends the divided rock typing to the whole oilfield. The estimated permeability as new pore-permeability relationship was applied to the geo-statistical inversion, which achieved the high-resolution spatial prediction of reservoir parameters and satisfied the fine reservoir characterization. It reduces the huge cost on drilling and coring, also provides a concise and effective approach to improve the reservoir estimation and production efficiency.
Exploring CO2-H2S Storage in Deep Saline Aquifers: A Case Study from an Offshore Gas Field in Malaysia. From Lab to Numerical Simulation.
Authors: Zaidin M.F. , Valtz A. , Chapoy A. , Tewari R.D.
Published: February 2024

Most carbon dioxide (CO2) storage schemes rely on storing CO2 in its supercritical state, free from impurities (SNC-Lavalin Inc., 2004). The X field reservoir model, like many other existing reservoir models, assumes pure CO2 injection for its numerical simulation of CO2 storage. However, one of the gas sample analyses from the X field revealed the presence of trace amounts of hydrogen sulfide (H2S,) ranging from 500 to 1000 ppm. Given the limitations of the separation technology, there is a potential scenario where CO2 might be co-injected with H2S for storage. Understanding the impact of this H2S within the injected CO2 stream is crucial for ensuring the success of Carbon Capture and Storage (CCS) operations (Basava-Reddi et al., 2014; Wang et al., 2011). There is a possibility of CO2 being co-injected with this H2S for storage. The effect of the contaminant in the injected CO2 stream needs to be accessed to ensure the success of the CCS operation. The alterations in the base CO2 solubility can ultimately influence storage integrity and capacity (Ahmad et al., 2023). While abundant solubility data for CO2 in water or brine exist in the literature (A Chapoy et al., 2004; Valtz et al., 2004; Ahmadi & Chapoy, 2018), limited data are available for this ternary CO2- H2S-Brine system. Therefore, the need to quantify the impact of H2S impurities on CO2 solubility is evident. In this context, extensive laboratory experiments were undertaken to address these uncertainties and further refine the X field dynamic model for enhanced accuracy.
A Systematic Approach for Re-Fracturing Optimization and Horizontal Well Production Performance Improvement in Unconventional Resource
Authors: Tang Yongliang , Zhou* Wei , Zhou Daiyu , Zhang Jie , Wu Yiming , Wang Peng , Liu Zhiliang , Li Guohui , Lian Liming , Fu Xiaotao , Du Hongbao , Li Zheng , Huang Xingning
Published: February 2024

Re-fracturing of unconventional horizontal wells is currently one of the main measures to restore the production capacity of old wells in most oil and gas reservoirs. Different repetitive fracturing reforming measures have been adopted in a low-permeability reservoir in Xinjiang, and the reforming mechanism is still unclear. By establishing a geomechanical model, a pore pressure-induced stress change mechanism model based on the finite element method, and a numerical model for unstructured reservoirs, the fracture morphology of repeated fracturing and the effect of the reforming measures were studied and analyzed. The results show that the fracture morphology changes significantly after volumetric repeat fracturing compared with first fracturing and ordinary repeat fracturing. The change of stress orientation before and after production is insignificant, but the change of pore pressure and stress has a greater influence on the morphology of hydraulic fractures. For the layers that have been production, due to the large energy deficit of the formation, it is difficult to extend the fracture, with shorter fracture lengths and larger fracture widths, while when there is a recharge layer, the fracture is easier to extend due to the sufficient energy of the formation. Wells with a higher location of the infill layer can effectively increase the degree of vertical utilization of the reservoir, which has a better effect on increasing production. The timing of repeated fracturing has an obvious influence on the effect of increasing oil production, and timely repeated fracturing is more helpful in improving the oil production capacity of a single well.
Machine Learning to Detect Oil-Based Mud Contamination in Oil Samples
Authors: Almalki F. F. , Alotaibi M. D.
Published: February 2024

Current methods to decontaminate bottom hole oil samples from Oil-Based Mud (OBM) poses many challenges as it requires analytical solutions. The existing analytical practices are highly subjective, as contamination results can differ depending on the approach determined by the user. The proposed method involves training a newly developed machine learning model to seek contamination patterns in an oil or gas condensate sample and highlight the contaminated samples. A python-based program that utilizes machine learning techniques to predict the concentration of OBM contamination in hydrocarbon fluids was constructed. The proposed procedure involved collecting 397 records of analyzed liquid C1 – C44 weight percent and whether they were reported as contaminated with OBM or not. The records were normalized and standardized on a column-basis, to preserve the confidentiality of the data. The model then, was trained on different algorithms, then was subjected to real cases to determine the model’s prediction accuracy. The train/test ratio was chosen to be 80/20 across different algorithms. The model was subjected to different algorithms. The algorithms were logistic regression, manual logistic regression, linear support vector machine (SVM), kernel SVM, Decision Tree, and Random Forest. The Manual Logistic Regression (MLR) algorithm resulted in an accuracy of 97%, and an F-1 Score of 94%. This shows that the MLR algorithm is the most suitable to be used in this prediction scheme. Moreover, this algorithm can be used as a quick method to get a sense of whether the fluid is contaminated or not. This illustrates the model’s significance in OBM contamination detection and making it the tool to be used in deciding the validity of a hydrocarbon sample. The current methods that are used to analytically decontaminate a fluid are based on analytical approaches, which depend on the person interpreting the results. Machine learning will resolve the issue of subjectivity in determining the contamination existence, increase the efficiency of the process, and is one step forward towards digital transformation.
Predicting Mineralogical Composition in Unconventional Formations Using Machine Learning and Well Logging Data
Authors: Gainitdinov Batyrkhan , Meshalkin Yury , Orlov Denis , Chekhonin Evgeny , Zagranovskaya Julia , Koroteev Dmitri , Popov Yury
Published: February 2024

Quantitative determination of mineralogy can be done using high-definition spectroscopic logging methods, however these methods are rarely used due to complexity and cost. Also, it is difficult to obtain mineralogical composition in unconventional formations due to presence of kerogen and high heterogeneity and anisotropy of such formations. This problem can be resolved by utilizing Machine Learning algorithms based on well logging and thermal profiling data which can improve and speed up reservoir characterisation. Special wrappers such as Multioutput Regressor and Regressor Chain were applied to test several machine learning models and strategies of well logs combinations on multiscale data from an unconventional formation in West Siberia to predict mass and volumetric fractions of minerals obtained from Litho Scanner. RMSE and MAE were used as regression metrics. To validate the results, theoretical model was used to calculate thermal conductivity based on mineral volume fractions and compared with experimental data. Regressor Chain showed better performance for weight fractions prediction when data was scarce. The Gradient Boosting Regressor encapsulated within a Regressor Chain exhibited the most favorable outcomes in relation to the precision of mapping. The evaluation contrasting the ML-based model with the LithoScanner exhibited an average discrepancy of 0.026, as measured by the RMSE metric.
Role of Ecosystem Partners to Make Nonmetallic Downhole Tubulars a Reality
Authors: Aladawy A. , Malkawi A. , El Shamy O.
Published: February 2024

Nonmetallic downhole tubulars offer a longer lifetime alternative than their steel counterparts while eliminating corrosion concerns and lowering total cost of ownership. However, making them a reality requires a rigorous ecosystem with multidisciplinary skill sets and technology expertise.

This paper discusses the ecosystem challenges that face the development of such tubulars starting from academia and research institutes to complement expertise and computer computational power for design, modeling, and simulation, to material suppliers and manufacturing facilities for the pipe prototyping. This study also includes testing houses and standardization organizations for the pipe testing and qualification program as well as the needed logistics for transportation and storage, ending with the system implementation service providers for the qualification, deployment, operation, and retrieval of the downhole tubular systems.

A detailed workflow for the integrations of the required ecosystem as well as examples are explained, and light is shed on the essential industry partnerships to make it happen. Focusing with examples on the Middle East region, multiple gaps need targeted localization of multidisciplinary capabilities, establishment of third-party startups and nourishment of small and medium enterprises, joint ventures, and joint industry programs. Specific focus was shed on the role of different ecosystem partners including pipe manufacturer, service provider, well operator, material suppliers, software developers, academia & research institutes, test houses, standardization institutes and logistics providers. Their contribution through the different phases of development, installation, lifetime, and retrieval of composite tubulars was described.

This publication is believed to be the first that tackles such complex problem with a holistic overview and way forward to solve. With such recommendations, nonmetallic downhole tubulars would be made possible with the proper ecosystem in place.
Experimental Visualization of Downhole Drilling Vibration Using Industrial Drilling Dynamic Recorder
Authors: Maitra E. K. , Al Dushaishi M. F. , Sugiura J. , Jones S.
Published: February 2024

Housing miniature sensors within bottomhole assemblies (BHA) have proven to provide crucial real-time and valuable post-well analysis for drilling dynamics optimization and vibration mitigation. The downhole dynamic behavior of the BHA is often misinterpreted and requires special data preprocessing for effective optimization. The objective of this work is to establish an understanding and standardization of downhole vibration data analysis using a scaled laboratory BHA equipped with a high frequency industrial drilling dynamic (DD) field sensor and trajectory mapping station. A novel downscaled test assembly adhering to the geometric and material property relations of a field-size BHA section was designed and manufactured to investigate the nature of lateral vibrations. Axial excitation, i.e., weight on bit (WOB) fluctuation, and rotation were induced respectively using an electromagnetic shaker and an electric motor. A downhole drilling dynamic recorder was housed near the bit-end. BHA trajectory was mapped using inductive displacement sensors at multiple locations, along its length, to test the capability and accuracy of the DD field recorder. The wellbore structure was built with transparent material for direct visual correlation between the BHA movement and the drilling dynamic recorder. The results from this experiment provide precise and insightful visual information for the BHA movement correlation with the dynamic data acquired from a continuous high-frequency downhole field sensor. Real-time induced axial WOB fluctuation was recorded using a high-frequency drilling dynamic recorder and the induced axial force was distinctly addressed with the level of magnitude and frequency. The BHA vibration behavior of high-frequency accelerations, critical lateral vibration phenomena such as whirling motion, angular velocity (RPM) responses, and vibration behavior shift at the harmonic frequencies were characterized in relation to both the nature of induced forces at the bit and the acquired downhole field sensor data. The novel experiment establishes both quantitative and qualitative relations between DD data and physical vibration behavior during drilling. The experimental results showed the benefit of drilling dynamic recorders and it is accurate in predicting severe lateral vibrations while for the first time providing visual representation to aid in vibration data interpretation.
A Systematic Approach to Drilling Performance Improvement and Risk Reduction in Horizontal Wells
Authors: Wang Xiaoliang , Zhou Bao , Wen Liang , Qin Hao , Yang Junqi , Qin Guangming , Guo Xiaosha , Wu Yangsong , Huang Xingning
Published: February 2024

The Fuman oilfield is the main block of the Tarim oilfield to increase reserves and production. However, the wells in this area are ultra-deep, and many sets of complex and difficult-to-drill formations are encountered in top-down drilling, which brings significant challenges to the drilling engineering. Based on triaxial and shear experiments, transverse isotropy theory and weak surface criterion, and integrating the mechanical damage under stress unloading and hydration in drilling wells, a well wall stability model under the synergistic effect of stress unloading and hydration has been established, and an analysis of the factors influencing the stability of the well wall of the formation has been carried out. The study shows that stress unloading occurs after drilling. The greater the confining pressure and axial stress, the more significant the shale strength weakening caused by stress unloading, and the magnitude of stress unloading is positively correlated with the degree of mechanical strength weakening. The more developed the laminations are, the easier it is to damage along the laminations. When stress unloading occurs, the weak structural surface acts synergistically with stress unloading, and the higher the degree of shale strength weakening is, the worse the mechanical stability is and the higher the risk of well wall instability. External fluids are prone to intrude along the bedding and promote shale hydration, making the shale mechanically less stable and reducing the well wall's stability. The effect of stress unloading on shale formation collapse pressure mainly occurs in the pre-drilling stage, and the effect of hydration damage on well wall instability mainly occurs in the late drilling stage. Beddings, stress unloading, and hydration affect the well wall stability together. The shale well wall stability model under stress unloading-hydration synergistic effect considers the influence of the three comprehensively. The prediction results align with the drilling actuality with good reliability after the field application test.
Erosion Mitigation During Testing of High Deliverability Gas Well - A Case Study
Authors: Bashir I. A. , Inam S. , Sana-ul-Hussnain M. , Shafee I. , Aijaz M. , Ullah R. , Haider M. , Qureshi H. A. , Khan M. , Ahmad H. , Awan F. R.
Published: February 2024

The paper details the case history of a highly challenging, high deliverability exploratory gas well that was successfully drilled and completed in the frontier region. The 4,900 m well encountered multiple reservoirs during drilling with severe losses in the topmost prospective limestone reservoir formation. Subsequently, well was cased and Drill Stem Test (DST) was performed to test reservoir deliverability. During testing, severe leakages were encountered at the Surface Well Test (SWT) equipment due to high flow velocities, coupled with Loss Control Material (LCM) and formation cuttings flowback. This led to internal erosion in the equipment, resulting in frequent uncontrolled hydrocarbon release into the atmosphere, posing a high risk for personnel. Consequently, frequent shut-ins for maintenance were necessary. Steps taken to mitigate this issue are discussed and recommendations are proposed for future encounters.

A systematic approach was adopted to offset the effects of erosion and consequential leakages. Points were identified which were prone to leakages, and successively, steps were taken to minimize bends. Non-intrusive thickness monitoring was carried out regularly to detect reduction(s) in the pipe schedule preemptively and take appropriate measures. Erosional velocities were computed for different pipe sections and sufficient back pressure was applied to reduce flow velocity. Other measures such as reducing flowing times at higher chokes and acid stimulation were also carried out to minimize the impact of, and dissolve, the particles eroding the equipment.

Significant improvements were observed after taking preventive measures – Non-Productive Time (NPT) was reduced, and the remaining job was carried out safely without any impact on the personnel, equipment, environment and the Company reputation. Accuracy of the collected data also improved, as particles had inadvertent effects on flow measurements.

An extensive study was carried out followed by on site actions to reduce the erosional impacts on a high pressure, high deliverability gas well. Important conclusions were drawn. Safe practices were developed to avoid future occurrences in high volume wells. A combination of steps such as re-arrangement of SWT setup to minimize bends, monitoring pipeline thickness using ultrasonic thickness meter, minimizing flow duration at high chokes, calculating downstream pressures required to limit erosion and applying back pressure, and performing acid stimulation can all help future operations to be carried out safely without any untoward downtime. In addition, dissolvable / degradable LCM could be opted in future wells, and wellbore hydraulics could be further studied to ensure optimum hole cleaning for effective cuttings removal.
Multilayer Perceptron Modeling for the Prediction of Gas Condensate Composition Changes During Pressure Depletion
Authors: Embaireeg Aisha
Published: February 2024

The production of the gas condensate in addition to the gas itself can improve the recovery factor of gas condensate reservoirs, as well as increase the economic feasibility of the reservoir. Hence, the prediction of the variation of the fluid properties of gas condensate reservoirs requires an accurate study of reservoir fluid characteristics during various pressure stages. The changes in produced gas composition during production are usually obtained from Pressure-Volume-Temperature (PVT) tests such as the Constant Volume Depletion (CVD) tests, provided the sample is available. Therefore, this study aims to develop an intelligent model to anticipate these changes in gas condensate composition during depletion stages, especially in cases where sample availability or reliability is limited.

A large data bank comprising of CVD tests for 141 of gas condensate samples at various pressures, temperatures, and compositions has been used to develop a multilayer perceptron neural network model to estimate the depletion changes in gas condensate composition from its initial composition. Furthermore, in order to demonstrate the effectiveness of the proposed model in cases where the Equation of State (EOS) is not accurately calibrated, a comparison was conducted between the proposed model and the EOS for all gas condensate samples.

The statistical error analysis show that model has high degree of accuracy to predict the gas composition within three depleted pressure stages. The model produced an average absolute percent error (AAPE) of 19.55% and 9.27%, and a coefficient of determination (R2) of 0.953 and 0.970, for sweet and sour gas condensates, respectively. Therefore, the model has an excellent agreement with the experimental data. All gas condensate samples in the data bank which were used to develop the model have also been used to study the accuracy of the Peng-Robinson Equation of State (PR-EOS) in comparison with the proposed model. The errors of the PR-EOS prediction of changes in the produced gas condensate composition resulted in an AAPE of 15.68% and 19.18%, and R2 of 0.936 and 0.828, for sweet and sour gas condensates, respectively. The accuracy of the proposed model in predicting the changes in the produced gas condensate composition is favorable in comparison with PR-EOS. In addition, the model eliminates uncertainties related to splitting and characterization of the plus fraction and inclusion of the binary interaction parameter correlations which are required for EOS.

The neural network architecture used in the proposed model exhibits a remarkable similarity to the experimental data. Furthermore, the model is highly practical, as it can be implemented within a programming language to rapidly estimate the gas condensate's compositional variation at different pressure stages.
Natural Fracture Modeling in Unconventional Dadas-I Member for 3D Seismic Survey in the Southeastern Turkey
Authors: Orlov Artem , Özkul Canalp , Bensenouci Fethi , Yazaroglu Ekrem , Mengen Ahmet Ergun , Yilmaz Ismail Omer
Published: February 2024

Silurian age Dadas-I shale exhibit low porosity and extremely low matrix permeability. Acting as one of the main source rocks in South-East Turkey, it represents a self-sourced unconventional play. This study aims at using an approach spanning from seismic to image log data analysis and 3D geomechanics to develop a discrete fracture network model (DFN) to provide new insights into distribution of hydrocarbons since only developed natural fractures make this reservoir producible.

The study uses borehole image log data acquired in the Dada»-I interval of 3 exploration wells: X-1, X-6, X-9, 3D seismic and 3D geomechanical model. The main steps of the workflow are: 1).to use interpreted image logs and classify the fractures by fracture sets, 2).to evaluate possible fracture drivers, defined as any 3D properties that can be sensitive to fracture intensity information in the inter-well space, 3).to review this analysis in the context of the seismic structural interpretation and regional tectonic framework, 4).to employ geomechanical model for likelihood of local reactivation.

The analysis of natural fracture properties, together with petrophysical data, seismic interpretation, and stress regime enable to build a 3D DFN model, predicts fractures occurrence in the Dada»-I interval and used this output for dynamic reservoir modeling. The seismic attributes together with regional geology reveals two major sets of fractures: strike in ESE-WNW that relates to the first extensional tectonic event and the second with a strike NNE-SSW after paleo-stress rotation from E-W to N-S. Application of geomechanical model discloses that not all fractures should be considered as point of weakness. Only discontinuities that are favorably oriented to in-situ stress more likely to be hydraulically conductive. The result of this interpretation were used to predict hydrocarbon flowing zones. Accordingly, the selection of testing intervals on the basis of the highest density of fractures only is not a valid approach to determine prospective zones. In the current strike-slip/compressional regime, the fracture direction and dip is the main control to hydrocarbon flow with the best contribution from natural fractures oriented close to the present day maximum horizontal stress.
Effects of Vibrator Distortion Induced by Surface Lithology on Seismic Data
Authors: Zheng Jie , Sun Junhe , Meng Qingbin , Peng Ai , Lyu Shuai , Ma Fei
Published: February 2024

Evaluating the effect of vibrator distortion on seismic data is not straightforward, particularly in terms of how to control the high distortion caused by surface properties, which can affect progress if it results in unnecessary repeats of sweeps. A series of vibrator tests for 25 days were designed and carried out, including comparative tests on different terrains and surfaces, and all relevant data was collected, including but not limited to: wired similarity tests; wireless similarity tests; sandwich-box tests; VAPS data; and recorded raw sweeps. After analysis of the causes of high distortions, raw sweeps, and the stack, it was concluded that the impact of the distortion on the seismic data could be neglected. The final conclusion is therefore that peak distortion should not be used as a parameter for quality control. Nevertheless, if the average distortion together with the average force is more than 100%, this means that the source parameters are perhaps not suitable for the type of surface in the work area, and the force level may need to be reduced. The even harmonic is mainly caused by near-surface properties. As long as the quality of the recorded data is not affected, minor fluctuations in certain parameters should not immediately lead to the conclusion that the vibrator is faulty, but should continue to be closely monitored. Improved field quality control of vibroseis units will reduce unnecessary delays in production as a result of distortion measurements.
Enhancing Production Efficiency by Unloading Produced Water and Condensate Using Engineered Surfactants: Case Histories
Authors: Omer Mohammed , Busteed John E. , Fragachan Francisco E.
Published: February 2024

Due to the depletion of reservoirs, liquid loading has become one of the common issues in mature fields. It happens where gas rates are not sufficient to lift associated reservoir fluids to the surface. Foaming surfactant injection is one of the methods used to mitigate liquid loading and enhance stable hydrocarbon production. In foam-assisted lift, a well is offloaded by creating foam downhole by the injection of surfactants. Surfactants reduce the liquid surface tension resulting in foam creation when the gas mixes with water or condensate downhole. This results in reduced liquid density and reduces hydrostatic pressure, thereby increasing drawdown and increasing resulting production.

We have developed a method to design and evaluate foaming surfactant performance for unloading water and for unloading condensate. This method eliminates the potential to create stable emulsions (through high mechanical shear). Additionally, the methods outlined will avoid misleading interpretations where only the foam half-life has been considered. Falsely optimistic results, from a foam half-life perspective, can occur where certain surfactants result in "weak" foams that can maintain volume while "dropping" a large fraction of the "liquid" out of the foam. In this method, foam density, or foam mass versus time, is considered.

The study results in an evaluation through the measurements of the time to create a known volume of foam, the rates of foam dissipation, and liquid phase recovery rates. This analysis is done by plotting foam multiple parameters versus time. Finally, the observation of individual phase attributes is carried out.

This paper will introduce a unique methodology, process, and design to evaluate foaming surfactants for condensates and for produced water. This paper will also present the findings from foaming surfactant applications for unloading produced water in the Middle East, Asia, and unloading condensate from wells in Europe. This work will help in optimizing the design for foaming surfactants and, hence, help enhance hydrocarbon production.
Successful Application of Co2 Foam Fracturing Enables Paradigm Shift in Stimulation Strategy of North Kuwait Jurassic Depleted Reservoirs
Authors: Abdel Mohamed , Al-Mefleh SLB Kholoud , Al-Muhanna Danah , Fidan Erkan , Abdul-Samad Zamzam , Al-Salali Yousef , Ajayi Ayo
Published: February 2024

The first ever CO2 foam fracturing new technology in Kuwait Oil Company (KOC) history was executed flawlessly in late 2021. Three treatments were executed. Co2 Foam Fracturing proved its significant added value of improving productivity in deep depleted tight carbonate Jurassic reservoirs, enhance flow back, reduce water consumption and carbon emission, and enable early production plus improving operation efficiency and cost saving. The stimulation operation has proven to be a huge success for all multidisciplinary teams involved as preliminary results showed over 50-70% production increase compared to offset wells.

The main challenges of acid fracturing stimulation in depleted reservoirs are the need for extended formation cleanup to flow back the injected fluids via prolonging Nitrogen lift that add higher operational costs and intervention operations. Therefore, energetic high foam efficiency frac fluid becomes essential to assist flowback and retrieve pumped frac fluids from reservoir. To tackle these challenges, Carbon Dioxide CO2 is pumped in liquid phase as energetic fluid together with normal frac fluids. Due to CO2 liquid nature, high foam efficiency can be reached (40 – 50%) at much lower friction losses. So, it enables achieving pumping frac at high rates and high foam efficiency. The main benefits of CO2 Foam frac are better fracture cleanup due to expansion of the stored compressed gas in the liquid CO2, fluid loss control that is provided by foam, minimized fracture conductivity damage, and the increase in hydrostatic pressure while pumping that translates to lower surface pressures during injection.

The selected pilot well is in depleted deep tight carbonate reservoir area of North Kuwait Jurassic gas fields. The executed acid fracturing operation required close planning starting from Q1-2021. Many challenges faced from logistical issues, lack of infrastructure and CO2 resources for the multi-faceted operation due to COVID-19 pandemic limitations. These challenges were tackled ahead with the integration of technical and operations teams to bridge the knowledge gap and to enable executing the operation safely.

The pilot well’s net incremental production gain is estimated at 50-70% compared to offset wells, with improved flowback and formation cleanup with less well intervention. The resulting time and cost savings as well as the incremental well productivity and better operation efficiency confirmed high perspectives for the implemented foam acid fracturing approach. Another two CO2 Foam acid fracturing wells were executed with good results too.

This paper will demonstrate the value of CO2 foam fracturing in depleted reservoir and KOC experience post first application and its plans to expand CO2 Foam Fracturing application across KOC different fields.
Automated Wellhead Monitoring Using Deep Learning from Multimodal Imaging
Authors: Li Weichang , Ma Yong , Alerigi Damian San Roman
Published: February 2024

Wellhead growth caused by temperature and pressure effects during production can lead to severe consequences, causing well integrity failure and surface equipment damage, sometimes with catastrophic incidents at huge safety risks and economic losses. In addition, it may lead to unintended emission when pipe connections are damaged. This work develops multimodal imaging and computer vision- based methods for automated wellhead equipment health monitoring, notably wellhead displacement or growth detection and quantification. Wellhead equipment is imaged at the well site using optical and/or hyperspectral cameras if available. The captured wellhead imagery or video is then fed into a computer vision system for analysis to determine the wellhead health condition such as the amount of displacement or growth, using machine learning techniques. First a set of sample wellhead images are labeled with wellhead segmentation annotation or bounding boxes. The set of sample data are then grouped randomly into training/validation/testing subsets, according to certain partition ratio. We then construct semantic segmentation and object detection models; train these models on the training and validation subsets and then apply to testing data set for performance assessment. These trained models can then be applied to new wellsite imagery from permanent monitoring to extract wellhead equipment. The extracted wellhead equipment image is compared with the baseline wellhead image and dimension for growth detection and quantification. This removes interferences from background objects, ambient lighting variations and other non-equipment related conditions. We collected over 4000 sample well-site images that contain well-head equipment, among which we have labeled a subset of 1200 samples which are randomly partitioned into 900 training samples, 150 validation and 150 testing samples. After training and validating the Mask R-CNN model on the training and validation samples, respectively, the model is then applied on the testing samples. The training and validation performance in terms of Intersection over Union (IOU) reach 89% and 78%, respectively, and the test performance achieves 75% IOU. The segmented well equipment image is then compared with the baseline. After 2D cross-registering, we have achieved highly accurate prediction of displacement. This computer vision and image driven based approach for wellhead displacement prediction has great advantage over traditional thermos-stress model-based approaches in that it can detect displacement in real-time with high accuracy.