bluuebunny/crossref_metadata_2025 huggingface dataset:
Best Practices & Inhibitors Utilization to Prevent Sulfide Stress Cracking During Coiled Tubing Intervention in HPHT Sour Wells - A Case Study from Oman
Authors: Kumar Mayank , Abdullah Irfan , Pathak Himanshu
Published: February 2024

Coiled tubing (CT) strings are costly and long lead items which are prone to sulfide stress cracking (SSC) and stress corrosion cracking (SCC) when exposed to sour conditions. This paper provides the real-time observations where CT exposure to harsh sour well conditions led to the occurrence of premature failure in CT strings resulting from SSC. The paper will also describe how the application of improvised protection practices can lead to quantifiable enhancement of CT string life.

The paper will also explain the optimum grade selection for a CT string which remains the foundational factor influencing the life expectancy of the string and also outline best practices for CT string maintenance and management on and off jobs. Analyzing mass data of CT string utilization spreading over 200 plus well intervention CT runs shows the dominating control factor is the application of SSC inhibitors with unique scavengers to inhibit the corrosive environment the string is exposed to. Chemical protection in combination with appropriate fatigue derating was primarily evaluated in this study.

The results were exceptional having observed zero sulfide stress related failures resulting from exposure to hydrogen sulfide (H2S) concentrations as high as 70,000 ppm (7%) and high-pressure high-temperature (HPHT) conditions over a period of four years.

When it comes to the observation of coiled tubing life expectancy, the application of improved practices and procedures and the inference resulting from these practices, are time-bound and dynamic. The pre-job evaluation of the scope and wellbore conditions was a key turning point as it affected the job design. Specific jobs may have sour conditions, but with a lower partial pressure, the design of chemical protection can remain mild. Practices in this paper proved effective for a range of H2S and downhole conditions where H2S partial pressure was always above 0.05 psi. Concurrently, the study concluded that a scope that calls for prolonged CT string exposure to downhole conditions and prohibits access to pumping chemical, will not be the right candidate to implement the recommended practices in this study.

In conclusion, using effective scavengers and inhibitors along with implementing the recommended practices such as coiled tubing fatigue derating remained the basis for establishing the best protection practice for such unforgiving environments.

The study this paper presents dispenses an operating range for a coiled tubing to safely operate in specific high H2S environments. Implementation of synonymous design of chemical protection and coiled tubing derating routine establishes a moderate precedent for operating low carbon allow pipes in harsh sour environment while maximizing pipe life.
Innovative Logging Methodology and Design to Acquire Data in Challenging Multilateral Horizontal Wells
Authors: Aljuaydi H. S. , Mehmood S. X. , Aljassem H. M. , Miftakhov I. I.
Published: February 2024

Acquiring high quality downhole production contribution data for multilateral wells is essential to quantitatively evaluate reservoir performance. The complexity of multilateral completion design and unavailability of advanced logging tools act as innovation drivers to address existing challenges and enhance the current equipment menu in the market. This paper presents an upgraded version of both logging hardware and approach which ultimately resulted in significant improvements of lateral accessibility and data quality.

The upgraded logging tool utilizes the Total Flow Systems (TFS) which comprises of passive acoustic measurements and multiple active response sensors: temperature, and pulse neutron tools (PNL) in water flow log under different well conditions. The spectral acoustic recording system captures acoustic signals generated by fluid movement across a wide range of amplitudes and frequencies within an extended scanning radius. A numerical flow modeling was utilized to quantify inflows corresponding to temperature gradient changes within the active zones. The phase split is then integrated via simulation model given the inputs of water profile provided by PNL.

Implementation of the upgraded version showcases high-resolution production profile eliminating the need for mechanical spinners utilization. The generated numerical temperature modeling provided accurate interpretation to the effective production zones in the horizontal section. The analysis of the data collected from two laterals of well P-1 clearly demonstrates that the primary oil production comes from the motherbore, with the oil distribution profile being evenly spread along the entire open hole section. Temperature data from lateral indicates its subdued performance, characterized by a high water cut in the produced fluid. Quantitative analysis of flow profile for well P-2 suggests the top of the logging interval inside the motherbore was contributing around 80% of the oil inflow, whereas the lateral was dominated by uniform inflow. In addition, the majority of water inflow in the lateral was located across bottom depths. In both laterals, inflow zones detected by acoustic sensor during flowing condition had uniform signature. The absence of localized noise events confirms matrix flow without fracture flow contribution.

The new methodologies and upgraded hardware proved that it can be used as the new standard for production logging in challenging wellbores for production optimization and better reservoir management. The data analytics features improved the decision-making process and accurately represented the reservoir condition.
Predicting Interfacial Tension in CO2/Brine Systems: A Data-Driven Approach and Its Implications for Carbon Geostorage
Authors: Khan Mohammad Rasheed , Tariq Zeeshan , Ali Muhammad , Murtaza Mobeen
Published: February 2024

CO2 Interfacial Tension (IFT) and the reservoir rock-fluid interfacial interactions are critical parameters for successful CO2 geological sequestration, where the success relies significantly on the rock-CO2-brine interactions. IFT behaviors during storage dictate the CO2/brine distribution at pore scale and the residual/structural trapping potentials of storage/caprocks. Experimental assessment of CO2-Brine IFT as a function of pressure, temperature, and readily available organic contaminations on rock surfaces is arduous because of high CO2 reactivity and embrittlement damages. Data-driven machine learning (ML) modeling of CO2-brine IFT are less strenuous and more precise. They can be conducted at geo-storage conditions that are complex and hazardous to attain in the laboratory. In this study, we have applied three different machine learning techniques, including Random Forest (RF), XGBoost (XGB), and Adaptive Gradient Boosting (AGB), to predict the interfacial tension of the CO2 in brine system. The performance of the ML models was assessed through various assessment tests, such as cross-plots, average absolute percentage error (AAPE), root mean square error (RMSE), and coefficient of determination (R2). The outcomes of the predictions indicated that the XGB outperformed the RF, and AdaBoost. The XGB yielded remarkably low error rates. With optimal settings, the output was predicted with 97% accuracy. The proposed methodology can minimize the experimental cost of measuring rheological parameters and serve as a quick assessment tool.
The Utilization of 3D Printing in O&G Applications
Authors: Tavares J. A. , Aldakhil D. A.
Published: February 2024

3D Printing, also known as Additive Manufacturing (AM), is a manufacturing method that has revolutionized the production of engineered parts, by enabling companies from multiple different industrial sectors such as transportation and medical, and ultimately Oil & Gas to prototype or re-build and replace any part, regardless of geometrical complexity. Such disruption has been enabled by the unique characteristics of 3D Printing that allow for logistical flexibility and full independence when manufacturing parts to repair existing equipment and re-commission them for operation. This use-case is particularly valuable if the objective is to repair equipment considered obsolete, where finding replacement parts poses a challenge.

Detachment from the traditional supply chain is a unique strategic position for any company to explore in order to better predict and control its product output. More so in an industry such as Oil & Gas, where business and operation continuity are heavily prioritized, and any potential disruption can cause significant losses of revenue and fossil fuel supply. 3D Printing, due to the flexible nature of the technology, can play a considerable role in tackling challenges that compromise Oil & Gas production by minimizing downtime. While exploring the potential of 3D Printing, decreasing downtime is a consequence of localizing production of components, which in itself results in reduced lead times and cost compared to importing the same commodities from overseas markets.

Also, from a carbon emission perspective, 3D Printing is a less wasteful process than subtractive manufacturing (i.e. milling and machining) and facilitates the production of complex geometries. In addition, 3D Printing plays a role in eliminating physical storage of components as a support technology that enables digital platforms serving as databases of digital replicas for existing components to be ready for 3D Printing on demand. This type of database is typically designated as Digital Inventory. This means that in the future, companies may be able to keep lower stock of critical parts knowing they can be replicated as needed. Consequently, less energy will be consumed by negating the need for large stock-up activities, and with the relocation of manufacturing activities the shipping carbon footprint is reduced.
Automatic Recognition of Homogeneous and Heterogeneous Reservoirs Using Deep Learning Technique
Authors: Retnanto Albertus , Wahid Md Ferdous , Indico Romeo , Paderres Janessa , Malyah Mohamed , Moussa Mahmoud
Published: February 2024

Reservoir analysis and characterization primarily rely on well test data to indirectly infer crucial reservoir parameters, including wellbore storage, permeability, storativity ratio, and initial reservoir pressure, by examining pressure responses from downhole. While prior studies have showcased machine learning algorithms’ effectiveness in accurately classifying reservoir models with over 90% accuracy, these efforts often have been limited to fewer than ten distinct models. This study introduces a streamlined convolutional neural network (CNN)-based architecture for deep learning, extending its capabilities to classify twenty diverse reservoir models. Pressure transient signals were generated for these models, encompassing both homogeneous and dual porosity reservoirs, each exhibiting ten unique boundary types, including infinite acting, circular sealing, constant circular pressure, single sealing, single constant pressure, angular sealing, channel sealing, channel constant pressure, U-shaped sealing, and rectangular sealing. Utilizing 300 pressure signals for each model, we harnessed well-test analysis software to simulate these signals. To accommodate variations in real field data duration compared to simulated data, we employed an outer product transformation of pressure transient and pressure derivative data, converting it into a grayscale image with values ranging from 0 to 1 and resized to 30×40 dimensions. This transformation preserved crucial patterns, regardless of magnitude and duration. Optimizing the CNN's hyperparameters, including filter number, size, max-pooling, and stride, was achieved through Bayesian optimization, resulting in a five-level deep ResNet architecture. Our model evaluation adopted an 85-15 data split for training and testing. The network's performance was assessed using key metrics, yielding impressive results, with an overall accuracy of 93.3%, sensitivity of 93.4%, specificity of 99.7%, and an F1 score of 93.2%. Furthermore, the model demonstrated consistent accuracy above 90% for fifteen of the twenty reservoir models, even when tested with diverse magnitude and duration pressure signals. This study's innovative approach highlights the effectiveness of a simplified CNN architecture in accurately classifying a broad range of reservoir models, offering reduced training time without compromising accuracy. These findings lay the foundation for future research aimed at further advancing deep learning's potential in reservoir characterization, promising valuable contributions to the field.
LLMs, Embeddings and Indexing Pipelines to Enable Natural Language Searching on Upstream Datasets
Authors: Martin James Lee , Zanuri M Nur Arif , Sockalingam Muthu Kumar , Andersen Eric
Published: February 2024

Large Language Models (LLMs) are attracting an enormous amount of interest at the moment in many domains. Their general nature, and ability to "understand" natural language, has already stimulated multiple areas of research at our company. Here we successfully demonstrate a Natural Language querying system, which is able to search a large repository of unstructured exploration data. The system supports follow up querying on the returned results, plus automatic summarization of content.

The system is integrated into our novel end-to-end data-mining platform, which continuously mines our unstructured exploration data for new changes and indexes the results. Important in our method are the enrichment processes that occur prior to use of the LLM. Our approach avoids usual "chunking" techniques, which in our experience results in inferior results, especially in the multiple domain areas of Exploration. By integrating our novel ontology-model AI in the enrichment of the initial Index, we drastically boost the performance of search resulting from the LLM steps.

In order to perform the search, key parts of our unstructured data, plus the query itself, need to be transformed into a vector form. This is performed using the embedding feature of the LLM. For this work, we had around 500,000 embeddings to calculate. To improve performance these were indexed in a leading Analytics Engine as a vector object, allowing fast search via cosine or Euclidian similarity. A custom dashboard was made to allow fresh searches of the vector datastore to be returned for further analysis.

Our current search time across 500,000 embeddings is under 20 milli-seconds. Our custom dashboard returns the top matches for further interrogation and analysis. This includes follow-up Natural Language question support on the returned matches for summarization tasks and other customised querying. Since our exploration-specific, ontology model is able to tag each piece of data with over 40 exploration-specific labels, we are able to cross-examine the LLM returned results with the tags. Agreement on a range of queries - ranging from targeted, highly specific questions to general, open-ended queries - was surprisingly good.

Natural Language based querying of our unstructured data is opening a whole new approach to data discovery in our company. Tailoring it to the exploration domain has required specific domain expertise and a novel ontology-model be used to ensure relevant prompts and query results. Obtaining search results quickly has also required expertise and fine-tuning. Future directions include ingesting more data, scaling the support infrastructure and further capability enhancement.
Experimental Identification of Permeability Reduction Factors in the Danish North Sea During Produced Water Reinjection
Authors: Kurbasov Maksim , Feilberg Karen L.
Published: February 2024

Loss of formation permeability during reinjection of produced water is a challenge which occurs frequently in offshore and onshore oil and gas production. Because of this, production companies need to develop measures to increase the injectivity of wells, such as hydraulic fracturing and acid treatments, which incur additional operational costs of oil production. The risk of injectivity loss is higher in tight reservoirs such as chalk. This study presents laboratory experiments using pressure and temperature controlled core flooding techniques to demonstrate how complete blockage of the chalk core pore space can occur. An analysis of precipitates formed during the injection of mixtures of produced water and seawater into a chalk core from the Danish North Sea was carried out, and an experiment with synthetic produced water is presented to identify the processes in the pore channels as precipitation of salt crystals occurs.

The study includes an integrated approach for studying sediments formed during the reinjection of produced water and seawater, namely studying the solid material using a scanning electron microscope (SEM), identifying the type of chemical compounds using X-ray diffraction (XRD), building thermodynamic models based on Extended UNIQUAC, and determining the ionic composition of effluents using High Pressure Ion Chromatography (HP-IC).

As a result of the analysis of sediments obtained on cellulose filters and laboratory core flooding experiments, it was established that the main factor in reducing permeability is likely the formation of inorganic sediment in the form of the compound Fe3Si. Understanding this allows the industry to take a more targeted approach to the process of treating produced water before pumping it into wells and reduce costs during well operation.
Limitations of Language Models in The Oil & Gas Upstream Operations
Authors: Alsultan A. , Razak F. Abdul
Published: February 2024

Language models has powered a lot of applications and developments in the past years in different domains. One language model that became popular in the recent years is BERT which is used as a base for many different natural language processing tasks such as classification and question answering. We wanted to apply BERT on some upstream oil and gas related text and study its performance on this domain. We decided to choose classification as a task to experiment with it. We tried a mix of domain adaptation and transfer learning to understand the potential of language models in general and BERT in specific on our domain. Our results emphasized the need to create domain-specific datasets for the oil and gas world and subsequently build models using them.
Balancing Surface Facilities & Well Design to Comply with Cap-Rock Integrity in CO2 Storage Projects: Experience from UAE
Authors: Xu Siqing , Vantala Aurifullah , Ali Imtiaz , Baslaib Mohamed , Al Keebali Aaesha , Perumalla Satya , Samy Ayman , Concilio Luciana , Singh Hemant , Mascagnini Carlos , Pandya Shekhar , Kasekar Hiren
Published: February 2024

CO2 injection is different than oil and gas injection, where CO2 phase changes are very likely to occur inside tubing at various operating conditions, especially in depleted gas reservoirs during early stages where reservoir pressure is low, and CO2 can change from supercritical to gas phases at various depths inside tubing when flow approaches perforation intervals. This is a dynamic and transient behavior, and modelling wellbore pressure and temperature during the life of the well (early, mid and late) is essential and can provide better understanding of the potential risks that are associated with CO2 injection and the appropriate actions and mitigation plans. Severe cooling due to phase changes and Joule Thomson effects inside tubing, as well as cycling of cooling and heating conditions, can negatively impact well and reservoir integrity including cap rock.

This paper presents a case history of a CCUS project in the Middle East where wellbore pressure and temperature modelling showed a significant impact on surface facility design, and a geomechanical model was constructed to investigate cap rock integrity as well as the safe operating envelope for CO2 injection. The workflow included modelling of seven time-steps to represent CO2 injectivity performance and pressure/temperature profiles inside tubing from start to the end of the project. In addition, sensitivity analysis was considered for CO2 impurities at each time step. The results feed the geomechanical study to ensure reservoir and caprock integrity during injection as well as surface facility design and mitigation plans.

The results showed that high purity CO2 can yield significantly higher injection rates compared to a low purity scenario. During early stages of injection, low bottomhole injection temperature was observed. Injection under low temperature may jeopardize reservoir and caprock integrity. Potential solutions to mitigate such issues are to consider heating CO2 at surface at early stage or to use smaller tubing size which may impact the injection capacity. Early stage during injection is the most critical period and may impact completion design and reservoir/cap-rock integrity. Hydrate risk assessment was conducted and showed that surface conditions are close to the hydrate risk zone. The operational philosophy has been modified to consider a surface heating unit to be used during the early stage of injection until reservoir pressure builds up.

This paper presents the importance of detailed wellbore pressure and temperature modelling during CO2 injection and how this can impact the integrity of the project as well as the operational philosophy. Workflow and risk assessment are also presented.
Sun Powered Green Hydrogen - A Comparative Analysis From The Kingdoms of Morocco and Saudi Arabia
Authors: Szemat-Vielma Waldemar , Scheibz Jurgen , Kasraoui Nihad , Al-Omar Faisal
Published: February 2024

The renewable energy sector, particularly the solar PV generation, is to play a key role in the energy transition and decarbonization process and the green hydrogen production is a subsequent element of this decarbonization process as a clean energy carrier. When power output from these renewable installations exceeds the grid requirements, instead of stopping the energy generation, that power surplus can be used to produce hydrogen by electrolysis process.

Despite being a technically simple process to produce via electrolysis, fuel cost and equipment are the two most significant economical elements to consider as part of the LCOH equation and act as economical boundary conditions. Combining an in-depth analysis while applying the financial modeling toolbox, this project has evaluated specific conditions for solar PV installations in Morocco and Saudi Arabia markets in terms of a techno-economic analysis for a potential investment for green hydrogen production in 2021 as well as near future projections in 2023 and 2025.

The most potential application of green hydrogen production and usage is to decarbonize heavy industries (e.g., cement and steel) that cannot be electrified but this will require an extensive transport infrastructure with low-cost incidence for the green hydrogen to be an economically viable solution. Near future projects will require public funding in the form of grants or tax redemption to scale up to economical maturity.

After carrying out a detailed financial modeling and a discounted cash flow valuation model, the resulting LCOH for Morocco is $3,2695/kg while Saudi is $1,5757/kg as of the end of 2021 with a projected reduction to reach $2,3678/kg and $1,4417/kg respectively in 2025, which means that by 2025 both countries will be below the $1,5-2,5/kg green hydrogen threshold, on a competitive level with fossil fuels, enabling both countries to grasp unique commercial opportunities to lead the implementation of a green business models towards a hydrogen economy, and eventually a net zero world.

The paper will elaborate on the rational driving the need for green hydrogen, will elaborate on the geopolitical framework supporting this emerging business and dives in with the techno-economic analysis while creating a 2023-2025 look-ahead.
Strategies to Design Fit for Purpose EOR Pilots by Integrating Dynamic and Static Data in a Highly Heterogeneous Oolitic Carbonate Reservoir
Authors: Khan Mohammed Yunus , Al-Murayri Mohammed Taha , Eadulapally Satish Kumar , Al-Mayyan Haya Ebrahim , Alrukaibi Deema , Al-Kharji Anfal
Published: February 2024

Umm Gudair Minagish Oolite is a heterogeneous carbonate reservoir with random intermittent micritic units forming low permeability barriers to fluid flow. The facies, permeability variations and barriers have limited lateral extension. Therefore, different strategies need to be designed to implement accelerated fit-for-purpose polymer injectivity pilots without compromising the proper assessment of key parameters such as polymer injectivity, polymer adsorption, resistance factor, in-situ rheological properties, volumetric sweep efficiency, incremental oil gains, and polymer breakthrough.

The field is divided into geological sub-regions based on reservoir scale heterogeneities by integrating static and dynamic data. The pilot location for each region is selected such that it shows minimal variations in reservoir properties in terms of facies, permeability, and extension of barriers. Simulation results were analyzed for each considered pilot area based on injectivity, pilot duration, oil peak rate, overall polymer performance and economics. Using these parameters, pilot design and locations are ranked while emphasizing the need to reduce the number of additional required wells to de-risk polymer flooding as a precursor for commercial development.

Based on time-lapse saturation logs different sweep zones are identified and correlated with the facies. The maximum oil swept is observed in clean Grainstones. The facies characterization along with production data were used for defining the geological sub-regions. The pilot performance was analyzed using high-resolution numerical simulation for each geological sub-region, using high-salinity produced water. Thereafter, pilot design and locations were ranked based on dynamic performance. The best performing polymer injectivity pilot, with limited well requirements, was selected for field implementation including one injector and one producer with an inter-well distance of 80m. The envisioned pilot duration is 6 months showing promising incremental oil gains from polymer injection compared to water injection. Besides incremental oil gains, the utilization of produced water for polymer injection improves operational efficiency and cost optimization.
Far Field Petrophysics Utilizing Deep Resistivity High Definition and Ultra-Deep Resistivity 3D Inversion in Thin Laminated Reservoirs
Authors: Manuaba Ida Bagus Gede Hermawan , Ghanim Rayan Adil , Bikchandaev Eduard , Taher Ahmed
Published: February 2024

Resistivity measurement is a fundamental tool for evaluating reservoir fluid and rock properties. This technology has evolved over the past few decades to address different challenges. It has been utilized widely as the primary geo-steering measurement given its large depth of investigation compared to other logging measurements. Near wellbore resistivity measurements are used for evaluating the saturation and identifying the borehole fluid while deep and ultra-deep resistivity tools are used for well placement.

Conventional deep azimuthal resistivity tools can map resistivity at a distance with limited depth of investigation and a limited number of contrasting layers. In contrast, ultra-deep resistivity technologies see deeper but don't generally detect thin layers. Recent development of new deep resistivity high-definition technologies, with associated inversions, has resulted in higher depth of detection with better definition of the thin layers. This is due to improved antenna design, better signal-to-noise ratio, and multi-frequency transmission. 3D inversion of ultra-deep resistivity has enabled reduction of near wellbore cell size for mapping thin zones. This helps define more formation detail while reducing the uncertainty in the resistivity values mapped for these layers.

An innovative workflow for far field saturation has been developed based on acquired resistivity data, combining multiple sources from near wellbore measurements, offset logs and inverted resistivity data. The workflow has been tested on conventional deep resistivity data, and while this provides a good indication of the saturation away from the wellbore it doesn't provide a detailed definition of the thin layers further away from the well trajectory. Deep resistivity high-definition inversion and 3D inversion of ultra-deep resistivity with reduced near wellbore cell size has been tested in multiple wells and clearly provides improvement in the detection of multiple layers away from the wellbore, including with thin layers. It has been used as an input to generate far field saturation maps and has provided analysts with a better definition of the reservoir properties at a distance, which is key to current and future development plans.

This paper demonstrates the improvement in far field saturation mapping utilizing the high-definition deep resistivity inversion and 3D inversion of ultra-deep resistivity with reduced near wellbore cell size. This represents a step change in far field petrophysics.
Comparing Product Performance Through Time of Silicone Based Defoamer and Polyalcohol Based Defoamer in Aqueous Fluid
Authors: Bu Khawwah M. A. , Alrammah S. A.
Published: February 2024

Foaming in water based mud is considered a major issue that drilling operations might encounter that causes several issues such as decreasing in mud weight and makes the assessment of the fluid volumes very difficult, and complicated while fluid mixing process at the correct rate and at the required density. During operation, lower gel strength reduces the degree of foaming encountered, but will often compromise the cutting support and removal properties of the mud. The addition of the defoaming and/or antifoaming agents such as polyalcohol based, and silicone-based products is used to eliminate the foam. Alcohol based defoamer composition is hydrocarbon chain and oil carrier fluid, and silicone-based defoamer composition is silica-based material with the organic solvent then coating the material with polydimethylsiloxane (PDMS). Two generic water based mud formulations were mixed where foam was guaranteed then, two different types of defoamers were added separately to eliminate the foaming. Antifoam timing and amount were observed during the addition. The process included examining the defoamer performance with brine contaminated formulations.

In this study, comparison of results between polyalcohol based and silicone based defoamers in two types of salts provided an evaluation of the behavior of defoamer through comparing antifoam timing and amount were studied by conducting performance tests within laboratory conditions.
Zwitterionic Surfactant as Shale Swelling Inhibition Additive in Water-Based Drilling Mud
Authors: Gbadamosi Afeez , Murtaza Mobeen , Patil Shirish , Kamal Muhammad Shahzad , Hussain Syed Muhammad Shakil
Published: February 2024

Shale swelling occasioned by the interactivity of clay minerals and water-based mud (WBM) remains a major source of concern during drilling for hydrocarbons. This is because the interaction has the tendency for causing formation damage, wellbore instability, and well blowout if not properly managed. Herein, novel synthesized carboxybetaine zwitterionic surfactant was investigated as a shale swelling inhibition additive in WBM. Firstly, the effect of the surfactant on the electro-kinetic and dispersion stability of the WBM was determined. Moreover, the surfactant effect on the rheological property of the WBM was measured. Subsequently, linear shale swelling test was conducted to evaluate the clay hydration inhibition potential of the surfactant using an OFITE dynamic linear tester. Experimental result depicts that the carboxybetaine zwitterionic surfactant reduced shale swelling by ≈ 42% compared to the conventional WBM. Moreover, rheological test indicates that the WBM containing surfactants exhibited lower shear stress compared conventional WBM. Finally, the surfactant-stabilized drilling mud displayed high colloidal and dispersion stability compared to conventional WBM. The mechanism of zwitterionic surfactants for clay hydration and shale swelling inhibition additive was perused. Overall, this study indicates that betaine-type zwitterionic surfactants are suitable as shale swelling inhibition additive in WBM.
Waterflood Optimization in Nahr Umr Sandstone Reservoir in Bahrain Field Using Ensemble of Novel Analytical Techniques
Authors: Aljowder A. E. , Alkuwari F. , Konwar L. , Mohamed A. , Alowainati E.
Published: February 2024

The Sandstone reservoir is on production since 1941. Water injection (WI) started since January 2013 and full fledge injection commenced through injectors in 2013. A maximum injection rate was achieved in 2015. Following start of WI, oil production has increased continuously and peaked in 2017. Both liquid rate and oil rate have declined continuously since 2017 due to lower injection stemming from facility issue. The liquid rate has declined 30% during 2020 to 2022 resulting 41% decline in oil rate as a result of reduction of water injection by 62% due to facility issue. The objective of the paper is to optimize the water injection rate based on historical voidage, current voidage and also future water injection rate based on predicted oil and water production. Besides decline curve analysis (DCA), modern analytical techniques are used to evaluate sweep and recovery and remaining potential in the reservoir.

The X-plot technique is used for estimating water influx from production data. Water injection requirement is worked out based on water influx, historical and current voidage. The methodology also shows a workflow to estimate future water production en route to future water injection requirement. Additionally, volumetric sweep is estimated using production data, Y-function and extended X-plot techniques. Field derived fractional flow curve is generated to determine displacement efficiency. Besides recovery estimated from volumetric sweep and displacement efficiency, hydrocarbon pore volume (HCPV) injected vs. post-waterflood cumulative oil production is used to determine ultimate recovery at 99% water cut. When combined with DCA results, the analysis yields incremental water flood recovery potential.

The injection requirement to redress the historical voidage is determined assuming 5 years of injection and injection required to address current voidage. Therefore, the current total injection requirement is 50% higher than current injection rate. As water production increases, the maximum injection requirement is 75% higher than current injection rate by 2025 and thereafter progressively reduces as oil and water rates decline. Volumetric sweep estimated by different techniques are in good agreement with each other, lending credence to the methods used. The ultimate water drive and waterflood recovery using sweep and displacement efficiency is 51% and compare favourably with HCPV injected method. In contrast to remaining waterflood recovery of 8% of original oil-in-place (OOIP), the DCA analysis indicates a recovery of 2.7% of OOIP. This underscores the fact that current injection is not adequate and additional injection will be required to improve production. This is in good agreement with the current production performance of the reservoir showing both gross and oil rate decline.

This study underscores the importance of fit-for-purpose use of analytical tools to diagnose, analyse and improve waterflood performance.
Post-Match Ensemble Analytic Techniques to Investigate Posterior Forecast Uncertainties
Authors: Forte S. Lo , Rossa E. Della , Aly M. Amr. , Grilli F. , Radice E. , Vecchio E.
Published: February 2024

Ensemble history matching is an effective tool which allows for uncertainty quantification, but it could be very time consuming. We show that some basic techniques can be used to reduce involved workflows complexity and computational time. The objective is to present how the statistical analysis techniques, commonly used for initial ensemble quality check, can be applied to the ensemble of matched models to highlight the major source of uncertainty and post-match forecast impact.

With ensemble history matching an initial set of different models is conditioned to production history. To create the initial ensemble, all relevant uncertainty parameters are selected after sensitivity and included using a workflow, which can become very complex and time-consuming, inside a geo-modeler. More realizations are requested until the initial ensemble passes quality check analysis. An iterative algorithm, requiring a preset number of iterations, has been used to match the ensemble of model realization. The matched models are then used to determine the forecast production profile range. Different methods are then applied to understand the most impacting parameters on forecast outcomes.

The uncertainty analysis workflow is applied to a tight gas field with some years of production history (early development phase) that is going to be integrated with other fields as a part of an LNG project. Initial sensitivity analysis helped to reduce the included uncertainty parameters preserving the original forecast uncertainty range but reducing the workflow complexity and the needed computational time. All updated ensemble realizations have been used for further analysis on the forecast. Analyses performed on matched ensemble (including correlation analysis and clustering) were used to highlight the most impacting parameters on forecast outcomes and to select most representative cases to be inserted in integrated asset models. The choice of the cases has been guided by the most relevant outcomes for the projects (like total field gas production and gas plateau length) and most relevant impacting parameters.

The presented uncertainty quantification workflow and associated analytic techniques have led to a roughly halving of the standard time needed for the uncertainty analysis (after history match) of the tight gas case. Further analysis on the posterior uncertainty helped in risk mitigation and its consecutive decision-making by changing the original drilling schedule to accelerate data acquisition that can reduce the forecast uncertainty range.
Integrated Charge Assessment in Nashpa Block Area of Kohat Fold and Thrust Belt of Pakistan
Authors: Bartha A. , Ali M. , Siddiqui F. H. , Bhatti A. I. , Joppen T. , Nawaz M. A. , Akhtar S. , Khan I.
Published: February 2024

The main goal of the 2D basin and petroleum systems modeling (BPSM) study was to understand the petroleum systems elements and processes in Nashpa Block Area of Kohat Fold and Thrust Belt (FTB) of Pakistan, including generation potential of presumed source rocks, timing relationship between hydrocarbon charge and trap formation, role of the faults in hydrocarbon migration and accumulation, uncertainties related to input parameters, and last but not least to support the risk matrix concept to increase the confidence in assigned risk values.

The area of interest (AOI) of the 2D BPSM study is located west of the Indus River in the Kohat Basin which together with Potwar Basin are considered as part of Sub-Himalayan Tectonic Province demarcated by the Main Frontal Thrust (MFT) to the south and the Main Boundary Thrust to the North (MBT). The Main Boundary Thrust displaced the Mesozoic rocks over Eocene to Miocene rocks in the footwall. This major compression created the Kohat and Potwar foreland basins defined by thin-skinned tectonics of thrust and duplexes (Wandrey et al. 2004).

In the area with very complex structural movements the potential traps located on regional antiforms are the consequences of the compressive deformation triggered by the collision of the Indian plate into the Eurasian plate. In addition to the general north-south shortening, additional displacement events took place as well, which significantly enhanced the properties of the Mesozoic-Paleocene reservoir rocks through fractures (Wandrey et al. 2004).
Drilling The Longest Reentry Well in Khafji Joint Operations with Smart Fit for Basin Drilling Services
Authors: Al-Thaqafi S. H. , Al-Jassam E. H. , AlDosari M. , Muheeb M. R. , Osman A. M. , Al-Majed A. , Al-Dossary M.
Published: February 2024

To boost the target production and to achieve the Kingdom of Saudi Arabia and the State of Kuwait visions, Khafji Joint Operations (KJO) started evaluating new projects to boost their daily production in 2020. KJO decided to drill an exploratory reentry well to confirm the existence of oil beyond the known area in one of its carbonate formations. This objective required drilling the longest reach reentry well in the field.

The well challenges were to sidetrack from a vertical existing well with high-collision risk and passing through shaly formations associated with wellbore stresses and instability. Also, other difficulties were drilling across zones with high risks of lost circulation and geological uncertainty. To effectively ensure reducing the drilling cost and flawless operations, an integrated solution from different domains including drilling, geomechanics, petrophysics, mud logging, well placement and drilling fluids were included in the project.

Drilling challenges have been reviewed from offset wells and previous drilling performance was analyzed to deliver a comprehensive well risk assessment. The risk assessment dictated different measures to be taken in the planning phase. First, a geomechanics study was used to optimize the trajectory design and define trajectory limits for each formation. Second, engineering analysis included a finite-element drilling dynamics study to evaluate combining the rotary steerable system (RSS) bottomhole assembly (BHA) with logging while drilling (LWD) to predict the dynamics and dogleg capability. Third, recommended drilling practices and roadmap were provided with contingency plans for anticipated risks. Finally, different drilling, formation evaluation, and geosteering services were included in the well design requirements to provide challenging well profile, real-time data assurance and decision making on reservoir fluid characterization, reservoir description, and detailed fracture characterization, which will be critical for proper well placement and delivering well objectives.

The well was successfully drilled, meeting the required objectives and delivered the longest reentry lateral in Khafji offshore using the latest generation of RSS and full geosteering and petrophysical technologies which enabled a cost-effective well placement solution in a complex geological environment with high uncertainty. The well delivered the required reservoir evaluation objectives, which opens additional future opportunities with similar application in Khafji field.
An Analytical Solution to Pressure Build-Up for CO2 Injection into a Saline Aquifer: The Impact of Stress-Dependency of Permeability
Authors: Liu Hui-Hai , Han Yanhui , AlRuwaili Khalid M. , AlTammar Murtadha J.
Published: February 2024

Aquifer pressure build-up caused by CO2 injection has been an important issue for evaluating the safety of CO2 geological sequestration in saline aquifers. Although several analytical solutions to aquifer pressure evolution have been developed, an important factor, the stress dependency of the permeability, has been ignored in them. This study develops a new analytical solution by incorporating the dependency based on the commonly used quasi steady-state flow approximation. Following some previous studies, CO2 plume evolution is calculated from a principle that for a given CO2 injection rate, the plume evolves in such a way that the injection pressure at the injection well is in its minimum. The stress-dependent permeability is expressed with the two-part Hook's model. The analytical solution gives the thickness of CO2 plume and aquifer pressure as functions of time and location. It also provides bottom hole pressure of injection well as a function of time for a given CO2 injection rate. The impact of stress-dependency of permeability depends on several factors, including the aquifer stress sensitivity and pore pressure changes during the CO2 injection. The dependency generally reduces the pressure build-up for a given injection rate because the increase in pore pressure enhances aquifer permeability.
Enhancing CO2-H2S Storage Predictions in High H2S Fields: Improving Model Accuracy and Kinetic Rate Information
Authors: Zaidin M. F. , Amin S. M. , Azuddin F. J. , Razak A. A. , Mohsin N. , Pin Y. W. , Tewari R. D.
Published: February 2024

In the context of carbon capture and storage (CCS), the presence of impurities, such as Hydrogen Sulfide (H2S), in the injected Carbon Dioxide (CO2) stream poses a significant challenge (Bennion and Bachu, 2008). This challenge is particularly pronounced in carbonate reservoirs due to the potential precipitation of the minerals (Ahmad et al., 2023). Such precipitation can have detrimental effects on carbonate rock properties, including a reduction in rock porosity and permeability (Labus & Suchodolska, 2017; Clark et al., 2018). Ultimately, these changes can impact injectivity and storage capacity (Wang et al., 2012; (Zaidin et al., 2018), making it essential to comprehend the geochemical reactions involved, which are demonstrated as follows:
How Complex Lithology Schemes Affect Drilling Rate Prediction: Machine Learning Study
Authors: Gamal Hany , Omotunde Omokayode , Duarte Miguel , Mohamed Omar , Elkatatny Salaheldin
Published: February 2024

Drilling operations for oil and gas wells are considered one of the highest operating costs for the petroleum industry, and hence, implementing the new technology-based systems is highly required for cost reduction and efficient functionality performance for the drilling system. This paper introduces a machine learning study for the role of composite lithology schemes on drilling rate prediction as it is one of the common practical challenges of developing machine learning models for predicting drillability rate. A field case study introduces artificial neural networks (ANN) and extreme gradient boosted trees regressor (XGBoost) machine learning models to develop and validate a drilling rate prediction. The dataset incorporates surface rig sensor parameters such as weight on bit, drilling rotation speed, pumping rate and pressure, torque (as input parameters), and drilling rate of penetration (as the predicted output parameter). Confirmed ground truth data, including lithological characteristics and formation tops, complements the dataset. Two wells’ data contribute to model development, while blind unseen well data validates the models. The training dataset encompasses complex lithology formations sandstone, dolomite, anhydrite, limestone, and interbedded shale. The methodology follows a comprehensive workflow covering data preparation, filtering and cleaning, statistical analysis, feature engineering, model development, parameter optimization, and accuracy assessment through coefficient of correlation, average absolute percentage error, and root mean squared errors. The results showcase the high accuracy of the developed machine learning models (coefficient of correlation exceeding 0.99) during training and validation while the blind testing showed R of 0.83 and 0.89 for XGBoost and ANN respectively. The study explores the impact of complex lithology schemes on drillability rate prediction, employing data analytics and machine learning models. Real-time drillability rate prediction, facilitated by this model, serves as a technical guide for optimizing drilling parameters, enhancing performance, and achieving optimal mechanical-specific energy.