bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital Wellbore Stability Prediction with Machine Learning
Authors: Liu Peng , Li Jian , Chen Bo , Yan Gongrui , Lei Qihong , Liang Lin , Huang Yansong , Zhao Haipeng , Wang Gaige , Sun Maoyou
Published: February 2024

One of the main challenges during drilling is wellbore instability. Traditionally, geomechanical model construction and wellbore stability (WBS) analysis are manually executed by geomechanics experts for well planning and drilling. The procedures are usually complicated and time-consuming due to subsurface complexity, and the results highly depend on the executor's expertise. This makes WBS analysis far from ideal and automatic. In this study, we present a physics-incorporated machine learning method that performs WBS analysis in a simple and automatic way. First, it characterizes and digitalizes subsurface geostructures geometry by labeling formations and its lithology. Then, it trains a digital geomechanics model using a series of machine learning algorithms with existing data, such as geology, well logs, drilling data, and geomechanical data. The rock mechanical properties, including rock elastic modulus and rock strength, are trained as formation material property models which describe the changing patterns in each formation. The formation pore pressure and in-situ earth stresses are trained using a physics-based hybrid algorithms, taking into account formation compaction and tectonic settings. Lastly, wellbore stability along any planned well trajectories can be predicted using this digital geomechanics model to identify drilling risks, optimize safe mud weight, and hence improve drilling practices.

This digital approach was tested and validated in a shale oil field in Ordos Basin, China. In this field, horizonal wells are drilled targeting a shale oil reservoir, this requires pre-drill WBS analysis, which usually takes several weeks following a manual methodology. With the developed new method, the digital geomechanical model was trained with seven surfaces representing different geological formations and well data from six existing vertical wells. The digital model and WBS results, including formation collapse pressure, mud loss pressure and breakdown pressure, were then compared against manual results calculated by geomechanics experts using traditional methods. The digital results matched well with manual results. The comparison demonstrated the applicability and reliability with a learning accuracy of over 99%. With this digital model, the geomechanical properties and WBS analysis of five planned horizontal wells were accurately predicted and proved consistent with actual drilling results. Another significant advantage is the high computational efficiency and reduced need for supervision. In this case, the digital machine-learning method reduced the WBS analysis time for five wells from weeks to hours. This field case confirms the effectiveness and efficiency of transferring domain knowledge and data into digital models, it enables support for massive cluster horizontal drilling activities on well pad and field scale.
Identify Thin Reservoir Thicker than One Meter by Integration of Seismic and Logging Data
Authors: Gan Liqin , Chen Jianbo , Hou Yawei , Xie Yue , Shi Hongfu
Published: February 2024

As the oilfield development progresses into the middle and later stage, thick sand reservoir with good quality has reached high water-cut. More attention has been paid in thin reservoir development but its characterization is very difficult. At present, the lower limit of reservoir thickness detected by seismic is five meter. This paper proposed a new method to identify thin sand thicker than one meter by integration of seismic and logging data.

Seismic data has a high lateral resolution which can reflect lateral changes of reservoir, while logging data has a highed to integrate logging and seismic data to predict thin reservoir thicker than one meter vertical resolution which can identify reservoirs thicker than one meter. Therefore, it is propos. The first step is to screen out natural gamma ray (GR) which can differentiate sand and shale well then standardize, and transform wavelet to obtain low and middle frequency information which can reflect lithology. Secondly, well/seismic calibration, synthetic, and singular value decomposition were conducted to realize dynamic clustering of seismic waveforms. Then the common characteristics of seismic waveform and GR of the same layer and depositional microfacies were analyzed, and the database for microfacies, waveform and GR at well location was established. Finally, the initial model was obtained by simulation of the original 3D seismic volume track by track, comparison of the sample group with highest correlation, and weighted average of correlation of GR. Then the final inversion result was achieved through the iterative inversion under the guidance of Bayesian theory.

This method relies on high quality of GR data and has a good characterization effect on shallow water delta sedimentation in gentle slope areas. Taking P oilfield as an example, seismic inversion by above process showed that sand reservoir thicker than one meter can be detected. Five verified wells showed that the coincidence rate of reservoir prediction can reach up to 87%.
Improving Wellbore Stability by Leveraging Real-Time Spectral Gamma Ray Logs
Authors: Qatari Ammar
Published: February 2024

The objective of this paper is to examine the quantification effect of clay content on wellbore stability. Clay Content is known to have direct impact on hydrocarbon petrophysical modeling. The major factor to quantify clay content is to acquire spectral gamma ray logs. Utility of standalone gamma ray readings provides only qualitative clay content model in a form of clean and clay filled fashion rather than a quantified clay content. Assessing spectral gamma ray logs in real-time can be a potential factor contributing optimized decisions during the drilling operation. Dealing with abrasive sand environment require extra formation knowledge to formulate a better drilling plan that will ultimately result in a good hole condition. Wellbore stability is directly related to the formation matrix type that is being drilled. Clay content can also contribute to the calculation of permeability in the dynamic phase. To quantify clay content, spectral gamma ray logs are vital. Once spectral gamma ray logs are available, a full petrophysical lithology model is calculated featuring quantified clay content by volumetrically identify illite, orthoclase, and kaolinite contents. It directly works as a wellbore stability indicator leading drilling engineers to formulate real-time drilling plans minimizing wellbore stability issues. These plans can involve a better formulation of a mud system, change of inclination/azimuth, change of a drilling parameter such as rate of penetration to accommodate the current quantified clay content that is being drilled.

The use of a quantified real-time formation clay content indicator to minimize wellbore instability is vital to avoid operational complexities and non-productive operational time. A comparison of qualitative and quantitative methodologies of clay content types verses wellbore stability is investigated. This will ultimately showcase the importance of establishing a wellbore instability mitigation plan leveraging real-time spectral gamma ray logs. Formulations of mud systems, rate of penetration recommendations are made based on the results. A representation of potential mitigation of non-productive operation time is showcased highlighting the vitality of the proposed method. This paper examines the direct impact of the quantification of clay content on wellbore stability. The process can add value to reduce the costs associated with wellbore instability. It can also manage to lower the non-productive operational time caused by a changed bit due to an inefficient management of either drilling mud/rate of penetration due to an unquantified formation clay content.
Successful Water Conformance Control Using Newly Developed Smart Polymer Gels for Water Shutoff Application
Authors: Sharma Hemant K. , Almohsin Ayman M. , M Subaie Fehead , Kharrat Wassim
Published: February 2024

Water conformance is a common challenge in oilfield industry especially in water flood and active water drive reservoirs where water production has a significant impact on production economics, oil recovery and facilities constraints. Water shut-off (WSO) is an essential solution to delay or minimize water production however effectiveness depends on treatment efficiency. Selective mechanical water shut-off, (i.e. through tubing bridge plug with cement on top, mechanical plugs), the main technique used to solve this problem so far, has disadvantages, mainly sacrificing the section below the plugs in spite of having hydrocarbons below the plugs. A new and innovative treatment solution using chemical water shut-off was developed in-house, which when used, led to minimizing the unwanted water production with enhanced oil production consequently helping to improve economic life of wells.

The novel and fit-for-purpose chemical water shut-off technology extends the limitations of the currently available materials. The new system is designed to chemically bond the polymer to the rock surface. So, the system will have enhanced stability with superior performance. The fluid system is low-toxic and environmentally friendly. It is comprised of polymer gel and adsorption components. The gelant can be placed as a single phase, low-viscosity solution into the targeted formation zones. The gelation process is activated by the formation temperature. In-situ gelation takes place and the gel plugs pore spaces, thereby limiting undesired water production. The system can effectively prevent water flow at wide temperature range. The gelation time can be controlled by tuning the fluid composition. It allows a predictable and controllable pumping time, ranging from hours to days at a given temperature.

Post job, the well performance improved substantially with significant reduction in water production and consequent increase in the oil production. Production logs conducted pre and post operation have clearly confirmed shutting off the high permeable streaks which were contributing to high water production. With the changed production profile and rate test pre and post job, the technology was confirmed a success. In addition cleanout of the treatment fluid performed after the treatment, ensured full-bore accessibility of the lateral for future well intervention including logging operations.

This paper will describe in details the novel technology and its application, solution selection process and the operational deployment of the chemical including real-time monitoring of the fluid, as well as precautions taken to increase the success percentage.
Integration of Rock Typing and Neural Network Techniques for Accurate Permeability Prediction in Heterogeneous Carbonate Reservoirs: A Case Study from Abu Dhabi Offshore Field
Authors: Elabsy Eslam , Soliman Ahmed , Deng Lichuan , Al Abed Sundos , Montani Hiroki , Al Suwaidi Maddiah
Published: February 2024

Permeability prediction or calculation in heterogeneous carbonate formations is a challenging task due to the complexity of the rock properties and pore systems that are difficult to characterize accurately. In this study an innovative and efficient approach developed and used to overcome this challenge by combining rock typing and machine learning neural network (MLNN) techniques to accurately predict the permeability of heterogeneous carbonate formations.

The supervised machine learning approach based on a neural network algorithm trained on a large dataset of offset wells data. The input data for the algorithm included various rock properties from core and well logs analysis data. The output of the algorithm was a prediction of rock types and their corresponding permeability values based on the porosity and permeability equations. The MLNN model was trained using a backpropagation algorithm and validated using an independent dataset to ensure the accuracy and reliability of the results. The trained model was then used to predict rock types and permeability values for new wells.

The results of the study showed that the approach of using rock typing and machine learning neural network outperforms other traditional methods in predicting permeability in heterogeneous carbonate formations. The predicted permeability values were validated against actual measurements from core data and formation testing mobility, and the results showed a good correlation between predicted and measured values and demonstrating the model reliability. The use of rock typing provides a more accurate characterization of the reservoir and helps to improve the prediction of permeability. The study also revealed that the rock types and permeability values varied significantly across the carbonate formation, and the neural network model was able to capture this heterogeneity accurately by learn the complex relationships between the rock types and petrophysical properties, which resulting in improved permeability predictions. The predicted permeability values were used to generate permeability maps that helped identify areas with higher permeability values, which can be targeted for well placement to improve hydrocarbon recovery.

This approach lies in the integration of rock typing and machine learning neural network to predict permeability in heterogeneous carbonate formations. This method provides an innovative solution to the challenges associated with traditional methods, which often fail due to the complex nature of carbonate reservoirs. The approach is applicable to a wide range of carbonate formations, and has the potential to significantly improve reservoir characterization and production optimization.
Development of New Family of Perforating Charges for Stressed Rock Following a New Industry Standard
Authors: Grove B. , MacGillivray J. , Lavery J.
Published: February 2024

Executive Summary

We describe the development of a new perforating charge, and its ultimate qualification following a recently-released industry standard testing protocol. The updated RP19B Section 2 standard program (API, 2021) prescribes a total of 12 tests, wherein commercial batch perforators are shot into natural sandstone targets across a range of in-situ stress levels. Testing included rigorous preparation and documentation, and was third-party witnessed for full transparency.

Compared to a reference legacy charge (which had been originally qualified in a 19B Section 1 unstressed concrete target), the new charge improves penetration performance in the standard stressed rock target by up to 22%, while simultaneously increasing casing hole diameter by 32%.

The industry has increasingly recognized in recent years that charge performance in unstressed concrete does not reliably correlate to performance in stressed rock. This suggests that at least some concrete-optimized charges may not deliver optimal performance downhole. Therefore, the incorporation of a standard stressed rock target to charge development should lead to perforators better optimized for downhole environments, compared to charges optimized for unstressed concrete. Independent witnessing further increases confidence in the performance to be expected in actual wells. Improved charge performance downhole, in turn, should translate directly to more productive wells (or, in the case of injectors, wells with better injectivity).

The charge discussed in this paper is applicable to two common gun sizes – namely 4-5/8 (or 4-1/2) in, loaded at 5SPF, and 7-in (12SPF). As of this writing, the same methodology is currently being applied to the development of additional charges for other gun sizes, ranging from 2 to 7 inches.
Unsupervised Clustering on Paleochannel Detection with Curvature-Based Attribute
Authors: Hungund Bilal , Jiang Fan
Published: February 2024

This research focuses on the application of advanced seismic data processing techniques and unsupervised clustering algorithms to accurately identify and map paleochannels in the high-resolution Maui seismic dataset. Pre-processing steps were applied to enhance data quality, followed by the calculation of a positive curvature attribute used as input for multiple clustering algorithms. The study evaluates four state-of-the-art unsupervised clustering methods and employs Principal Component Analysis (PCA) for result refinement and differentiation. By analysing the effectiveness of the algorithms, we demonstrate that Fuzzy C-means and Birch provide optimal segmentation of paleochannels. This automated approach not only overcomes the challenges of noisy data but also opens avenues for similar applications in seismic interpretation. The research underscores the significance of the unsupervised method in facilitating efficient resource exploration, emphasizing the integration of PCA to enhance segmentation accuracy and reduce human intervention. This study contributes to the sustainable management and extraction of natural resources, underscoring the potential of advanced techniques in remote sensing analysis.
Use of Transfer Learning in Shale Production Forecasting
Authors: Misra Siddharth , Elkady Mohammad , Kumar Veena , Odi Uchenna , Silver Andrew
Published: February 2024

Production forecasting is vital in the oil and gas sector, empowering engineers with insights for effective reservoir management. This paper introduces the concept of Transfer Learning as a powerful tool in the domain of machine-learning-assisted production forecasting that accounts of 3D spatial distributions of three geological properties, namely porosity, permeability, and saturation, two completion parameters, namely hydraulic fracture height and length, and production constraint. Transfer learning efficiently leverages knowledge from one problem to improve generalization on another, especially when data is scarce and computational resources are limited. To demonstrate the utility of transfer learning, we evaluate two scenarios of transfer learning. The first transfer learning scenario demonstrates the generalization of the forecasting to cases with variable hydraulic fracture spacing using limited training data. The second transfer learning scenario demonstrates the generalization of the forecasting to cases with variable natural fracture spacing and natural fracture permeability using limited training data. Source dataset contained 2000 realizations, while the target dataset contained 20, 40, 60, 80, 100, 250, 500, or 1000 realizations to represent the scenarios of limited training-data availability. The study confirms the benefits of transfer learning when the training dataset size is small (generally less than 100 training realizations); however, under large training dataset size (around 500 or more training realizations), transfer learning is not needed. For the first scenario involving variable hydraulic fracture spacing, the use of transfer learning ensured that the source model can be trained on target dataset with 80 realizations for gas rate forecasting at an accuracy of 25% in terms of MAPE, and with 500 realizations for condensate rate forecasting at an accuracy of 12% in terms of MAPE. Similarly, for the second scenario involving variable natural fracture spacing and natural fracture permeability, the use of transfer learning ensured that the source model can be trained on target dataset with 250 realizations for gas rate forecasting at an accuracy of 24% in terms of MAPE, and with 500 realizations for condensate rate forecasting at an accuracy of 23% in terms of MAPE. This illustrates the potential of transfer learning in improving forecasting models with limited data using a well pre-trained model and enhanced hyperparameter tuning of the transfer learning model. For cases with 500 or more training realizations, transfer learning severely underperforms as compared to training a conventional machine-learning model from scratch. The paper explores two cases of transfer learning.
Innovative Solution to Dissolve Highly Challenging Iron Sulfide Scales
Authors: Charushin A. , Muhammad M. , Al Balushi I. , Albalushi S. , Hassan I. , Marine H.C.
Published: February 2024

Iron sulfide scales are an inorganic deposition, highly acid soluble, that block oil and gas wells, and are usually formed from H2S and reservoir/well reactive iron reactions, since HCl reaction with iron sulfide generates toxic and corrosive H2S gas, several alternative dissolvers including simple organic acids and more complex chelates have been utilized, our main objective is to evaluate the efficiency of a new synergetic of alternative dissolvers to provide economically satisfying treatments in Iron Sulfide scale defected wells.

To judge the dissolution capabilities of the new solutions, four different synergies have been evaluated for HPHT solubility considering the effect of pressure and temperature, screening soaking time and solution pH influences, with aid of catalytic additives at different scale to dissolver ratios to be able to conclude the optimum treatment design. Furthermore, corrosion testing for proposed treatment, and reaction product Fourier Transmission Infrared Spectroscopy (FTIR) were performed, to find out the treatment corrosivity and the functional group of reaction products.

An oil contaminated Iron Sulfide scale sample was tested, using different dissolver concentrations, with and without surfactant/solvent pre-soak, the best performed solution could achieve a minimum of 55% weight dissolution at 1:10 scale to dissolver ratio, a minimum of 61% weight dissolution at 1:20 scale to dissolver ratio, a minimum of 85% weight dissolution at 1:30 scale to dissolver ratio, by applying 6 hrs surfactant/solvent pre-soak and 24 hrs of static treatment soak, at 90 °C and 300 psig, the best performed solution was tested for corrosion and found corrosive at 250 deg F for 48 hrs on C95 corrosion coupon so that a compatible corrosion inhibitor was added to the treatment, the reaction products were reported water soluble. Based on the experimental data and recommended treatment design, fluid was pumped successfully and satisfying outcomes have been reported.

This paper presents novel and additive information to the existing body of literature on Iron Sulfide scale dissolution, the developed product is a blend of organic salts in cationic surfactant solution, that provides an outperforming alternate economic solution to currently used Iron Sulfide scale dissolvers with high affinity to other mixed scales, and at less concertation and soak.
Risk Mitigation and Drilling Time Reduction Practices with Optimized Wellbore Geometries Implemented in Offshore Shallow-Water Tertiary Wells in Gulf of Mexico
Authors: Barrera Edison , Perez Carlos , Gomez Jose Manuel , Alarcon Jose , Torres Maria Andrea , Zapata Abelardo , Laguado Pedro
Published: February 2024

Well construction operations in the Gulf of Mexico involve a high level of complexity in a high-risk environment. Although multiple operators and service companies have been drilling for decades and technology has evolved throughout this time, the wells targets become more challenging to drill and the technical level along with the economical benefit require new approaches to make projects profitable.

One of the key aspects to make drilling projects successful is risk evaluation and engineering applications to deliver producing wells efficiently. The offshore fields in the Gulf of Mexico require custom wellbore architecture and drilling practices to optimize time on well delivery without compromising well integrity standards and safety.

This paper describes the successful implementation of a performance enhancement process and risk mitigation strategies applied in optimized wellbore geometries in shallow-water fields in the Gulf of Mexico, for which customized de-risking engineering and drilling practices were introduced to accelerate well delivery. The subject fields in the Gulf of Mexico are considered as strategic in the development plan, in which the main drive is time optimization for early oil production, resulting in additional risk management planning.
Research on the Control Law of Balanced Oil Production of the Fluid Control Valve in Horizontal Section and a Case Analysis
Authors: He Zuqing , Zhao Jianguo , Zheng Haotian , Han Suo , Han Shuo , Huang Bensheng , Yuxi He
Published: February 2024

The existing balanced production model does not consider the characteristics of the fluid control valve and the influence of various factors on the opening, resulting in the lack of theoretical support or basis for the throttling control of the fluid control valve. Therefore, proposed a trapezoidal throttle valve with a large throttle differential pressure and anti-choking. On this basis, established calculation model of the flow rate and throttle pressure for horizontal well based on fluid control valves. Next, evaluated the throttling effect of the single-segment fluid control valve. Finally, analyzed the law of influence of the opening of fluid control valves at different positions in horizontal well on flow rate and throttle pressure of natural gas. It found that the maximum throttling differential pressure of the trapezoidal orifice reaches 0.395MPa, which is 18.98% larger than that of the traditional rectangular orifice. To achieve balanced gas production, the opening of the fluid control valve from the toe end to the heel end gradually decreases. The opening decreases with the increase of the differential pressure of each segment, and the maximum decrease is about 58.38%. The opening increases with the increase of valve distance, and the maximum increase is about 1.25%. Based on the flow rate and pressure model, a case is analyzed by dichotomy. After 5 times of adjustment, the difference between the production proportion of each production segment and the average production proportion is less than 2.7 %, and the balanced production is achieved. This case analysis provides an example basis for the previous theory and numerical simulation. The research results provide a theoretical basis for the fluid control opening control of horizontal wells and provide ideas for balanced gas production.
Casing Centralization—Planned Compared to Actual: A Kuwait Case Study
Authors: Fituri M. A. , Ali Y. , Al-Naqi A. , Saikia J. , Abdulrahim K.
Published: February 2024

Several reasons can contribute to poor cementation; however, proper casing centralization can lead to uniform mud displacement with cement and can mitigate some of the cementation problems. Cementing companies design their casing centralizations based on simulators, but they have never been able to validate quantitatively how their planned centralization compares to the actual, the only comparison made in the past was based on the overall cement quality measured by the cement evaluation tools. This can be reflective of the centralization program in some instances; however, in others, attributing quality to centralization can be misjudged or centralization may be mixed with other factors.

Not being able to directly quantify and evaluate each element in the casing and cementation program makes it challenging to improve or acknowledge each design. Relating the achieved cement quality to casing centralization gives inconsistent results. In many instances poor cement behind casing is because of other factors such as spacer design, fluid rheology, formation fluids, borehole geometry, etc., and is not only due to poor casing centralization.

Casing eccentricity or casing touching outer casing/formation is always observed on cement evaluation imaging logs, as they typically appear in a form of galaxy pattern. However, these galaxy patterns can only be considered as a qualitative indication of the casing being close to a casing or to a formation, as it will be shown later in the paper that galaxy pattern start to show on cement evaluation images when casing centralization is below 60%.

The new generation of ultrasonic flexural measurement tools can evaluate cement and at the same time provide third interface echo (TIE) measurement over 360°. The TIE measurement can evaluate casing centralization by evaluating the time between the first casing reflection (mud-casing interface) and the third interface reflection (cement formation interface) (Δt). When all the Δt values across all azimuths are equal, it is an indication of casing being 100% centered, and when Δt is 0 or small (casing touching formation) from any direction then casing is 0% centered. This Δt can also be converted to pseudo annulus thickness by assuming a velocity for the material behind the casing.

For an operator to improve their field casing cementation, a campaign was performed for evaluating casing centralization in multiple casing sizes for different well trajectories to set benchmark for future centralization improvements. Each casing had different centralizer size and different patterns.
Impact of Unsupervised Machine Learning and Seismic Attributes on Mapping Stratigraphic Traps
Authors: Alabbad Maitham , Alkhalifah Hussain , Awfi Sami
Published: February 2024

Mapping lateral variations of reservoir and seal is a key step for stratigraphic trap exploration. Hard data such as well testing results, fluid samples, pressure data, core analysis, and wireline log signatures are often used as evidence of lateral fluid barriers and facies boundaries which provide the lateral seal element of stratigraphic traps. Mapping these boundaries is challenging and requires the use of seismic data, especially in clastic reservoirs where sand bodies with different pore pressures or hydrocarbon columns can be mapped and correlated laterally, although they may not be interconnected.

One of the common workflows is to rely on seismic amplitude variations as a proxy for reservoir quality. Another method uses absolute acoustic impedance (AI) volumes with specific cutoffs derived from the rock physics template for the lateral seal definition. Also, red-green-blue (RGB) color blends of decomposed frequencies can be used to show the geomorphology of the reservoir unit and indicate lateral variations in depositional environments. These techniques require a prior knowledge of facies character on seismic to define suitable cutoff values. A good well dataset covering all expected facies is needed for calibration. Unsupervised machine learning techniques, on the other hand, are useful tools that guide seismic interpretation of large 3D datasets and reveal subtle trends automatically. The different unsupervised machine learning algorithms and their application on seismic data have been discussed in the literature. Mingjun and Cheng (2018), Lubo-Robles et al. (2019), and Owusu and Raef (2022) describe its use in facies classifications.

In this paper, two different seismic waveform classification algorithms are used to map potential stratigraphic lateral seal based on conventional land 3D seismic data. The first algorithm is Hierarchical Classification. The second one is based on K-mean Clustering. Methodologies, strengths, and weaknesses of these waveform classifications are discussed in this study. The derived seismic facies are compared with other products from conventional seismic methods such as seismic amplitude extraction maps, AI geobody extractions, and frequency spectral decomposition RGB blending.
Surface Microseismic Monitoring for Over 30 Wells in Changning Region Sichuan Shale Gas Play, China: The Discoveries
Authors: He L. , Chen Y , Wen R. , Fan Q. , Heng D. , Huang Z. , Wang B. , Abdel-Halim Amro
Published: February 2024

The surface microseismic monitoring technology has been utilized in Changning region Sichuan shale gas play for over 30 wells since the beginning of 2018. The microseismic events located in real time could help to optimize the pump schedule, estimate the diversion, and mitigate engineering risks. The wide coverage of the geophone channels allows for moment tensor inversion (MTI) which reveals the source mechanism, and so to identify the natural fractures more accurately.

The property of the microseismic events were integrated with the pump data, the completion design, the reservoir properties, the ant tracking results, as well as the production. The purpose was to find the optimum completion and fracturing design parameters, and more importantly to figure out which factors contribute the most to production. The MTI result provided more accuracy on identifying the natural fractures which are key for hydrocarbon flow but at the same time also bring challenges for hydraulic fracturing operation.

The surface microseismic monitoring technique provides decent results for reservoirs with TVD range of 2100-3850m in Changning region. The number and magnitude of microseismic events is dependent on natural fractures. The higher pump rate tends to generate fracture network with bigger width and so higher complexity and to achieve higher estimated stimulated volume (ESV). The MTI properties could help to identify natural fractures more accurately and calibrate the ant tracking results. The relative angle and distance of natural fractures to the well trajectory influences the ESV and so the production. The ESV of a single stage barely shows relevance to the production log, but the ESV of a well does illustrate relationship with production of the well, especially the accumulated production of 3 months.

The rich data of surface microseismic monitoring in over 30 wells in Changning region leads to better integration for reservoir properties, completion and fracturing design, microseismic properties, natural fractures as well as the production.
Accelerating Drilling Efficiency and Consistency: A Rig-Based Approach to Exponential Drilling Program Improvements
Authors: Major C. , Hall T. , Stogner W. , Kern S.
Published: February 2024

The evolution of the drilling process has been sustained by consistent innovation and development, bringing the industry to where it is today. Innovation moves us forward: data is processed faster, flat time is reduced, roles are redefined; but the vehicle to achieve a successful drilling program remains: the drilling rig. This paper investigates efficiency in its many forms, and the foundational role that the drilling rig plays in fulfilling the global need for energy.

Over time it has become evident in the industry that evolving the rig design process helps to achieve greater overall success of a well program. As less flat time occurs, well cycle time decreases, increasing the total amount of feet drilled per day and likewise increased hydrocarbon production in that same timeframe. Innovative advancements, through progressive iterations in rig design, have led to significant efficiencies in flat time reduction through initiatives like rig floor automation and installing a rig walking system. As these efficiencies become more realized, consistency is increased allowing for better forecasting of long-term well programs.

A historical account of pivotal drilling rig advances will be discussed in detail, along with specific applications of automation and a walking system that modern well programs require. Creating a new rig fleet using repurposed equipment is not only economical, but also a sustainable solution and of significant value as carbon emission reduction increases in importance across operator and supplier initiatives.

Introducing walking rigs to a global fleet has contributed to reducing well cycle times by over 35% from 2016 to 2022. What was once a three-to-five-hour release-to-spud skid, is now as low as an hour and a half through the use of walking technology.

The benefits to a standardized rig fleet will be presented: reducing inconsistency, increasing quality of support, and enabling an agile support staff; while providing a consistent platform to launch drilling applications where newly learned best practices can be immediately applied fleet wide.

Today's drilling environment requires an integrated approach, enabled by both people and technology. The drilling rig provides a strong foundation, as a platform for further technological innovation through automation. As the industry continues to evolve, the clear benefit of field-proven equipment and technology supports breaking through barriers in unconventional drilling.
Utilizing Integrated Network Model to Optimize Production and Prevent Halite Formation in Pipeline Offshore Gulf of Suez
Authors: Sayed M. El , Korish M. , Mousa H. , Moussa A.
Published: February 2024

Scale deposition has always been an irritating problem for oil and gas industry. Scale typically results from a chemical state of supersaturation, which results from changes in pressure, Temperature & PH across production system. At this time precipitation reaction takes place and the mass of scale deposition is a function of the available amount of cation and anion species related to subject scale. Typical forms of scale are calcium carbonate & Sulfate, Strontium & Barium Sulfate. Commonly Deposition rate for such types of scales is quite slow due to limited concentration of relevant species. For the case of halite deposition chlorides salts are abundant species in water that in case of deposition would result in huge mass that may block tubing, flow lines and pipelines in short time.

Subject Field is located offshore Gulf of Suez, rimary reservoir is Nubian sandstone at depth of more than 11000 Ft & Temperature of 330 F. Formation Salinity around 350,000 PPM. Typically, water is in equilibrium at reservoir conditions and high productivity of wells allows high wellhead temperature which keeps chloride salts soluble in water.

The field produces through two 12" pipelines to a central processing facility. s the flow goes heat exchange takes place and soon balances with seabed temperature. Scale tendency calculations showed Halite would precipitate at 90 F. which means the vast majority of pipeline will be subject to the risk of halite deposition. This matches with a biweekly to monthly increase in line pressure impacting production due back pressure on producers and at critical point emergency shutdown would trip to protect line integrity.

The problem has been persistent that shutdown was expected on monthly basis and logistics consumed to pump fresh water followed by soaking time and pigging to ensure line pressure kept to minimum otherwise it will not take long before line plugs again. In 2020 a detailed Network Model coupled with scale formation prediction tool to determine pressure and temperature profile in subsea pipeline and expected scale formation rate with distance from platform.

Network optimization model showed that commingling specific wells on one line and keeping others on the other line would result in decreasing the risk of halite scale, investigations showed some incompatibilities between water from different wells that can be managed with scale inhibitor.

Application of the outcomes from network model in the field resulted in maximizing production eliminating downtime due to line pressure increase and eliminated the need for biweekly fresh water pumping.
Coupled Geomechanics and Reservoir Simulation for Gigantic Naturally Fractured Reservoirs
Authors: Qiao Lei , Shen Meng , Almani Tameem , Al-Shaalan Tareq , Dogru Ali
Published: February 2024

The objective of this work is to introduce the features of a new in-house geomechanics simulator. In this work, the capabilities of the new simulator will be demonstrated by applying it to coupled geomechanics and reservoir simulation models based on large-scale naturally fractured reservoirs.

The 3D geomechanics simulator presented here utilizes the finite element method (FEM) and was developed in C++. The geomechanical grid is created by extending the reservoir into burdens. The simulator is fully parallel using the message passing interface (MPI) library. The geomechanics simulator is developed as a standalone module and the data exchange (pressure, permeability) with reservoir simulator is performed using the multiple program multiple data (MPMD) paradigm. Faults and discrete fractures are modelled by ‘equivalent material’ constitutive relation, in which multi-fractures are included in a straightforward manner. Plasticity is modelled using an implicit closest point projection algorithm in the finite element context.

The in-house geomechanics simulator supports three modes: initialization only, one-way coupling, and two-way coupling. It supports poroelastic and poroplastic modeling for intact rock and discontinuities (faults and discrete fracture), e.g., transversely isotropic material (TIV) and Mohr-Coulomb plasticity. It allows users to specify burden grids and stress boundary conditions. It provides permeability correlations for intact rock and discontinuities, including classic correlations like Kozeny-Carman. Users can also define the permeability correlation through table input. The simulator outputs 3D stresses, strains and plastic strains, displacements, and faults displacements and strains. Results can be visualized using industry standard tools as well as open-source tools such as ParaView. Results of the coupled simulation have been verified against industry-standard simulators. The simulation of a billion-cell model has been performed to demonstrate the capability of the in-house simulator for studying gigantic field models.

This work introduces a new in-house geomechanics simulator that is coupled to an in-house reservoir simulator. It demonstrates the capability to simulate billion cell reservoir models with natural fractures. Use of the multiple program multiple data (MPMD) paradigm via MPI offers a significant improvement in performance and efficiency when compared to a traditional file-based approach.
Upside Potential Risk Assessment in Clastic Reservoirs by Integrating Advanced Seismic Attributes
Authors: Salazar-Buendia I. A. , Elouair Y. , El-Hassan W. M. , Dass S.
Published: February 2024

This study shows the application of a comprehensive seismic interpretation and analysis to delineate the potential reservoir extension based on the integration of advanced seismic attributes. The results allowed to propose a delineation well based on the chance of success (COS) evaluation.

The reservoirs in the area of interest are rich in clastic sediments, deposited between fluvial and shallow marine environments. If we consider the burial depth, some of these target reservoirs may exhibit unusual high porosities. The overlaying stratigraphic formations are sequences of shallow water carbonates and evaporites.

The clastic sediments at the reservoir level are characterized by showing low acoustic impedance contrasts, and the shallower carbonate sequences usually trigger the generation of interbed seismic multiples during the seismic acquisition. Most of the time, the seismic image at the zone of interest is highly affected by the late arrivals of those interbed seismic multiples. Adequate seismic processing techniques have to be applied to eliminate multiples before moving forward with the seismic reservoir characterization task.
Maximizing Profitability Through Automated Production Optimization: A Game-Changer for Digital Oil Field Operations in Kuwait Integrated Digital Field (KwIDF)
Authors: Al-Rashidi H. , Ranjan K. , Lara R. , Muhsain B. , Singh S. , Tripathi D.
Published: February 2024

The paper outlines a comprehensive workflow for calibrating well models and optimizing well production. An automated well-model management system ensures data accuracy and timeliness by fetching information from the production database. Simultaneously, the well-production optimization process identifies opportunities for improving field development and production operations by analyzing real-time data and applying optimization techniques. The system empowers engineers with data-driven decision-making tools and provides recommendations for optimizing well parameters. The integration of multiple data sources, automated processes, and data quality control ensures the reliability of results. This automated approach enhances the identification of valid optimization opportunities and facilitates well performance management, leading to significant oil production gains and informed decision-making within the field.
Deep Learning for Predicting Evaporite Salt in the Mediterranean: A Case Study
Authors: Jiang Fan , Das Soumili , Ligeza Szymon , Osypov Konstantin , Wrobel-Daveau Jean-Christophe , Aikaterini Stathopoulou , Aristotelis Pagoulatos
Published: February 2024

One of the significant challenges in seismic interpretation is to accurately delineate subsurface features and quantify the uncertainty of the interpretation results due to the non-unique nature of seismic processing and imaging. Salt interpretation usually has limited resolution and relies upon an interpreter’s experience with a limited set of geological concepts. In seismic interpretation, especially salt interpretation, researchers have focused on improving the accuracy of pixel predictions by developing various neural network architectures, such as Attention U-Net (Oktay et al., 2018), Residual U-Net (Alom et al., 2018), Dense U-Net (Cai et al., 2020), etc. Studying uncertainty quantification of point predictions is important in assessing prediction quality. In this paper, we implemented multi-attributed assisted deep neural network to analyze seismic data for probabilistic interpreting evaporite salt in an exploration area in Mediterranean. In the study area, the newly acquired data express a high degree of complexity and no wells were drilled in this area. Salt tectonics knowledge in the study area is very limited and does not exhibit any know analogues. These factors bring challenges to interpretation. We show that our DL approach proposes a new way to train and predict different evaporite salt structures with good prediction results.
Automatic Kick Detection Using Artificial Intelligence
Authors: Sha Qihao , Ding Yan , Cui Meng , Cui Yi , Gao Reyu , Zhao Fei , Xing Shiwang , Lyu Congyue
Published: February 2024

Complications related to kick account for more than half of total well cost. Kick is unintended influx of formation fluids into the wellbore when the pressure found within the formation is higher than the mud hydrostatic pressure. If the event cannot be detected and controlled in time, they will lead to increased non-productive time (NPT) and costs. For example, the maximum killing pressure required will be more than two times higher if the kick is detected 15 min later. To kill a kick, actions like circulating the well with heavier/lighter mud will require the field engineers to stop drilling. Even worse, a kick can potentially result in a blow-out event, which can lead to loss of life and a company's reputation.

In recent years, people have made dramatic progress in kick detection. In general, they can be divided into physical-based models and data-driven approaches. For physical-based models, predicted values of the kick indicators are calculated and compared with the actual values. However, most of the physical-based models are unable to deal with complex situations, such as directional drilling and change of formation, temperature change, compressibility of fluids, etc. The pattern recognition approaches mainly consider the flow pattern. Moreover, these methods only recognize simple patterns, which might not be enough for real field application, where the data can be dirty and noisy. The models’ performance can also be severely impacted if the labels are unbalanced.

We propose to build a real-time alarm system for early kick detection using artificial intelligence and real-time drilling data. The goal is to use this automatic alarm system to predict kick events ahead of time and hence reduce NPT. First of all, the historical drilling data need to be integrated with daily drilling reports to recognize the kick events and routine daily operations. Second, calculating the possibility of kick from the drilling data, by applying our statistical method. Then, the preprocessed data is divided into training/development/test set. The Long Short-Term Memory Fully Convolutional Network (LSTM-FCN) need to be built, trained, tuned and validated with the drilling data.

We have already trained and applied the method in Chang Qing oilfield. It calculates the possibility of kick events and raise an alarm when the possibility surpasses a threshold. The accuracy for this field application is 90%.