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[Display omitted] • New drilling fluid formulation that contain Micromax and Perlite. • Enhancement of the rheological properties after adding Perlite. • Drilling fluid behaviour follows the Herschel-Bulkley and Robertson-Stiff models. • No effect of adding Perlite on pH and sag factor. The rheological properties of drilling fluids are considered critical criteria in mud design and selection. Rheological parameters greatly impact various drilling fluid functions, including the ultimate workover, carrying capacity, cuttings transportation, minimizing the loss circulation, etc. Therefore, different additives were used to control the rheological properties and improve the other drilling fluid parameters such as filtration and filter cake properties. In this work, amorphous glass rock known as Perlite was examined as an additive for manganese tetroxide water based-drilling fluids. Two dosages of manganese tetroxide were prepared namely 250 and 350 lb/bbl. The rheological properties were evaluated and implemented to assess the fluid behavior using four different models including Bingham plastic, power-law, Herschel-Bulkley and Robertson-Stiff models. In addition, the impact of Perlite on the manganese tetroxide drilling mud at varied densities were investigated in term of pH value and the sagging tendency. The obtained results showed that as function of adding the Perlite, the plastic viscosity was reduced while the yield point increased. The three parameters' models including Herschel-Bulkley and Robertson-Stiff models were the best in describing the drilling fluid with and without the Perlite. It was also observed that the Perlite has negligible effect on pH and the sagging factor was maintained in the safe window. Moreover, the filtration and the filter cake characteristics were enhanced upon the Perlite addition in both mud densities. [ABSTRACT FROM AUTHOR]

Weighting materials such as hematite are used to increase the density of cement slurries for different applications. Density variation (DV) across the cement column due to heavy particles sedimentation is a critical problem that results in disruption in hardened cement properties such as porosity and strength. In this work, two heavy-weight cement systems using Micromax and hematite were evaluated in terms of rheological properties, fluid loss, compressive and tensile strength, petrophysical properties and dynamic elastic properties. This study especially focused on the sedimentation problem associated with using hematite as a weighting material in well cementing. Different advanced techniques such as scanning electron microscope (SEM), nuclear magnetic resonance (NMR), and computerized tomography (CT) scan were used to investigate the potentiality of using Micromax to solve this problem. NMR, and CT-scan confirmed the results of the conventional method of DV that showed that the Micromax-weighted cement is more homogeneous with only 1.4% DV vertically along the samples. The Micromax-based system had lower porosity and permeability as compared to the hematite-based system. The Micromax-based cement was more flexible than the hematite-based system in terms of the elastic properties. Both cement systems showed a very similar performance regarding rheological and fluid loss properties. Micromax proved its potentiality to minimize the sedimentation problem encountered while maintaining the other recommended cement characteristics. • SEM, NMR, CT-scan proved the potentiality of using Micromax to solve the sedimentation problem. • The Micromax-based system had lower porosity and permeability as compared to the hematite-based system. • The Micromax-based system was more flexible as it has higher PR and lower YM than the hematite-based system. • In terms of strength, hematite had higher compressive and tensile strengths than Micromax. • Both systems had very close performance regarding rheology and fluid loss. [ABSTRACT FROM AUTHOR]

The precipitation (sagging) of solids from weighting materials, such as hematite is a challenge in high-pressure high-temperature (HP/HT) applications of drilling fluids. The result is inconsistent properties of the mud and poor fluid stability. This study provides a solution to hematite sagging in water-based mud for HP/HT wells which involves the combination of manganese tetroxide with hematite. A Field formulation of the mud was employed over a range of manganese tetroxide/hematite ratios (0/100, 10/90, 15/85, and 20/80%) in laboratory experiments to address the sag performance. From the sagging tests, the optimal combination ratio was determined. Then, density, alkalinity, rheology, viscoelasticity, and filtration behavior for the formulated mud were addressed. The tests were conditioned to 300 psi and 250 °F. The acquired results of static and dynamic sag tests indicated that incorporation of 20% manganese tetroxide solved the hematite sagging issue and bring the sag tendency within the recommended safe range. Upon the inclusion of manganese tetroxide, the density reduced insignificantly from 15.2 ppg with base hematite to 15.1 ppg for the 20% manganese tetroxide mixture and similarly the pH reduced from 10.5 to 9.9. The recommended 20/80% combination enhanced the yield point (YP) by 28% with 16% increment in plastic viscosity (PV) and resulted in a 10% improvement of the YP/PV ratio which usually is indicating better hole cleaning. Also, the gel strength was improved resulting in better solids suspension capacity. The filtration behavior of the formulation with 20% manganese tetroxide was superior compared to pure hematite since it resulted in 50% reduction of the filtrate volume and 43% improvement in filter-cake weight and thickness. This study contributes to develop a technically feasible and stabilized drilling mud using combined weighting agents for HP/HT applications, which economize the drilling cost and time by reducing the associated operational problems. [ABSTRACT FROM AUTHOR]

Because of porosity and mineralogy disparity, rocks have a broad range of bulk densities, a critical property that has an important role in rock characterization. Practically, the density of rock is estimated either via wireline logging or logging while drilling tools. However, these techniques are not always accessible, making the door open for using different predicting methods such as empirical correlations. The existing empirical correlations have substantial restrictions, which limited their accuracy and reliability. This work targets developing various artificial intelligence models to predict bulk density for complex lithology during drilling (i.e., at real-time). The artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques are used with the mechanical drilling parameters as inputs. This study uses a vertical well with 2912 data points from various lithologies including sand, carbonate, and shale to develop the models. Besides, a new empirical correlation for bulk density is developed based on the optimized ANN technique. A different dataset from the same tested field was used to validate the developed models. The obtained outcomes demonstrated that both ANN- and ANFIS-based models estimated the rock density with high fitting accuracy. The ANFIS approach results correlation coefficient (R) values of 0.99 with an average absolute percentage error (AAPE) values of 0.83 and 0.92% in training and testing processes. Whereas the ANN approach is slightly outperformed, as described by the highest R values of 0.99 and the lowest AAPE of 0.77 and 0.93% for training and testing processes. Also, a new empirical model for bulk density estimation was derived from the developed, optimized ANN-based model, which is applicable to the extracted weights and biases. The validation process indicates the robustness and reliability of the developed models with R values of 0.99 and AAPE of 0.92 and 0.96% for ANN- and ANFIS-based models, respectively. The outcomes of this work develop models with high accuracy for real-time prediction of bulk density, which can help in rock description in an adequate method with less cost, time, and errors. • Bulk density for multiple lithologies was predicted using artificial intelligence. • ANN and ANFIS techniques were applied. • The drilling mechanical parameters were used as inputs. • The reliability of the developed models was validated using different dataset. • The developed models are inexpensive, real-time, and have high matching accuracy. [ABSTRACT FROM AUTHOR]

When drilling deep wells and high-temperature high-pressure (HTHP) formations, the solids sagging and inconsistent drilling mud properties are widely encountered and exhibit severe technical and operational challenges. The aim of this work is to provide a new novel solution using "vermiculite" for sagging prevention and enhancing the mud stability. The vermiculite was introduced as anti-sag additive for water-based drilling fluid at elevated temperature. Various quantities of vermiculite were added (0, 1, 2, 3, and 4 lb./bbl) and the mud stability was accordingly evaluated by conducting the static and dynamic sagging measurements. The effects of vermiculite on mud properties were thoroughly investigated by assessing the mud density, alkalinity, rheology, viscoelasticity, and filtration behavior. The practical elevated temperature was simulated experimentally by conditioning the tests up to 250 °F. The results of this work showed an improvement on mud stability as the sag tendency was reduced by adding vermiculite. The amount of 4 lb./bbl vermiculite was enough to mitigate the solids sag at elevated temperature and bring the sag tendency factors to the safe values. The rheological and viscoelastic properties were notably improved with vermiculite. The 14% reduction on plastic viscosity and the increment of yield point by 36% with better gelling structure confirmed the ability of vermiculite to enhance the mud stability. In addition, the 4 lb./bbl-vermiculite contributed to the outperformed filtration performance with less filtrated volume and affinity to formation damage. The vermiculite containing drilling fluid yielded better filter cake characteristics with less thickness, weight, and permeability by 34, 35, and 66%, respectively. [Display omitted] • Vermiculite was introduced as a novel anti-sag agent for water-based mud. • New mud formulation was investigated for high-temperature high-pressure applications. • Mud stability was enhanced by adding vermiculite. • Adding vermiculite results in better rheological and viscoelastic properties. • The filtration performance was improved with vermiculite. [ABSTRACT FROM AUTHOR]

The stability of the weighting materials in the high-density water-based drilling fluid was remarked as a serious issue to be controlled. It can cause several problems including formation fracturing, circulation loss, well control issues, etc. The usage of an anti-sagging chemical as a drilling additive is one of the sagging mitigation methods. In this study, amorphous volcanic alumina silicate (perlite) was evaluated as an anti-sag additive for hematite water-based drilling fluid. Several experiments were conducted to assess the perlite efficiency including static and dynamic sag test, mud properties measurements such as filtration behavior, filter cake properties, rheological and viscoelastic behavior. The tests were conducted at elevated temperature (250 °F). Perlite concentration was varied from 0, 1, 2, 3, and 4 lb/bbl. The obtained results showed that the loaded perlite was able to improve the mud stability and eliminate the sagging under static and dynamic issues at an optimum concentration of 4 lb/bbl. In term of filtration properties, there was an obvious improvement that could be observed through the reduction of filtration volume with 50% and filter cake thickness. In addition, the perlite contacting drilling fluid was able to form a thin and dense filter cake layer. The filter cake porosity and permeability were decreased by 24% and 68%, respectively. The rheological and viscoelastic behaviors were improved as well by the addition of perlite. [Display omitted] • Usage of perlite particles to enhance the sag stability of Hematite WBM. • Impact of perlite particles on rheological and carrying capacity. • The recommended percentage of perlite particles to be added to Hematite WBM based different drilling fluid indexes. [ABSTRACT FROM AUTHOR]

The rate of penetration (ROP) is a critical factor affecting the process of oil well drilling optimization and the total drilling cost. This work introduces an empirical correlation extracted from the learned artificial neural networks (ANN) to assess the ROP across a vertical carbonate formation from five surface drilling parameters measurable through real-time sensors. The ANN was built based on real 220 datasets obtained from eight wells. The data from five of these wells were used to train the ANN model. Several sensitivity analyses were conducted on the model's parameters to achieve the best combination of these parameters. To enable real-time assessment of the ROP, a correlation from the leaned ANN model was extracted, which was tested on 92 datasets from the same training wells while unseen datasets from another three wells were used for validating the empirical correlation. The results showed that the ANN was effectively predicted the ROP with an average absolute percentage error (AAPE) of only 4.34% for the training data. Using the developed equation, the ROP was assessed for the validation data with an average AAPE of 6.75%. • ANN model was optimized for ROP estimation in carbonate formation using a date from entire field. • The ANN model predicted the ROP from real-time drilling parameters only. • The optimized model was converted to equation to facilitate the use of the model. [ABSTRACT FROM AUTHOR]

Allocated well oil rates are essential well performance evaluation. Flow meters are not reliable at high gas-oil ratio (GOR) and high water-cut (WC). Most of the available formulas are based on Gilbert-type formulas with neglecting the differential pressure across the choke. Adaptive network-based fuzzy inference system (ANFIS), and functional networks (FN) were used to generate different models to predict the production rates for high GOR and WC wells. A set of data (550 wells) was obtained from oil fields in the Middle East. GOR varied from 1,000 to 9,351 scf/stb, WC ranged from 1 to 60%. Around 300 wells were flowing under critical flow conditions, while the rest were subcritical. The developed AI models were compared against the previous published formulas. For each AI method, two models were developed for subcritical flow and critical flow conditions. The average absolute percent error (AAPE) in the subcritical flow for ANFIS and FN were 1.25, and 0.95%, respectively. While in the critical flow, the AAPE values were 1.1, and 1.35% for ANFIS and FN models, respectively. All developed AI models outperform the published formulas by 34%. The findings from this study will significantly assist production engineers to predict the oil rate in real-time without adding any cost or field intervention. • ANFIS and FN models were developed to predict the oil rates from the choke parameters for high GOR and WC fluids. • AAPE is 1.35% in the case of critical flow with water production and GOR up to 9,000 scf/stb and WC up to 40%. • AAPE is less than 1.25% in the case of subcritical models flow with GOR up to 5,000 scf/stb and WC up to 60%. • AI robust models can estimate oil flow rate with about ±1.35% accuracy error and (R2) of 0.92–0.95. [ABSTRACT FROM AUTHOR]

Determination of in-situ stresses is essential for subsurface planning and modeling, such as horizontal well planning and hydraulic fracture design. In-situ stresses consist of overburden stress (σv), minimum (σh), and maximum (σH) horizontal stresses. The σh and σH are difficult to determine, whereas the overburden stress can be determined directly from the density logs. The σh and σH can be estimated either from borehole injection tests or theoretical finite elements methods. However, these methods are complex, expensive, or need unavailable tectonic stress data. This study aims to apply different machine learning (ML) techniques, specifically, random forest (RF), functional network (FN), and adaptive neuro-fuzzy inference system (ANFIS), to predict the σh and σH using well-log data. The logging data includes gamma-ray (GR) log, formation bulk density (RHOB) log, compressional (DTC), and shear (DTS) wave transit-time log. A dataset of 2307 points from two wells (Well-1 and Well-2) was used to build the different ML models. The Well-1 data was used in training and testing the models, and the Well-2 data was used to validate the developed models. The obtained results show the capability of the three ML models to predict accurately the σh and σH using the well-log data. Comparing the results of RF, ANFIS, and FN models for minimum horizontal stress prediction showed that ANFIS outperforms the other two models with a correlation coefficient (R) for the validation dataset of 0.96 compared to 0.91 and 0.88 for RF, and FN, respectively. The three models showed similar results for predicting maximum horizontal stress with R values higher than 0.98 and an average absolute percentage error (AAPE) less than 0.3%. a20 index for the actual versus the predicted data showed that the three ML techniques were able to predict the horizontal stresses with a deviation less than 20% from the actual data. For the validation dataset, the RF, ANFIS, and FN models were able to capture all changes in the σh and σH trends with depth and accurately predict the σh and σH values. The outcomes of this study confirm the robust capability of ML to predict σh and σH from readily available logging data with no need for additional costs or site investigation. [ABSTRACT FROM AUTHOR]

Challenges associated with drilling operations are numerous and their adverse effect could lead to severe damage or even shutting down of the drilling operations. Wellbore instability among others is but the most encountered problem by drilling engineers when water-based drilling fluids (WBDF) are applied. This is because shale formations are full of active clay minerals which can lead to hydration and swelling once encountered by the WBDFs thus destabilizing the integrity of the wellbore. To combat this, most petroleum engineers have employed different inhibitors to reduce or stop the menace posed by these active clay minerals. The purpose of this study is to highlight in detail the essential types of clays mostly encountered in drilling operations, the swelling mechanism of the active clay minerals, comprehensive characterization techniques, and the differences associated with the applied inhibitors reported in the literature for tackling clay hydration and swelling. Based on the review, we recommend extensive evaluation between smectite and vermiculite clay minerals during analysis to determine the effectiveness of some of the characterization techniques adopted for the inhibition studies. Also, a methodology blueprint for specific clay mineral types is highly recommended for future researchers to help reduce the uncertainty associated with the reported outcomes of these active clay minerals. At least five characterization techniques such as linear swelling test (LST), scanning electron microscopy (SEM), x-ray diffraction (XRD), bulk hardness test (BHT), and wettability alteration test (WAT) to be conducted for any experimental procedure to be able to understand in detail the inhibitive mechanism of the applied inhibitors. Environmentally friendly inhibitors that are cheap, pollution-free with excellent stability in harsh environments are highly encouraged since they can be sustained and have good inhibition potential. • Discussion on the active clay mineral types used as additives in WBDFs and for shale inhibition studies. • Different swelling mechanisms encountered in drilling operations. • Characterization of the shale inhibitors reported in the literature using several techniques. [ABSTRACT FROM AUTHOR]

Gamma-ray logging (GR) is one of the most crucial elements in petroleum engineering that would assist evaluate oil and gas reservoirs and identify the formation lithology. GR can be measure for a retried core or by running the logging while drilling (LWD) tools. Both methods require a long time and high cost besides the complexity of applying them. In this study, three artificial intelligence (AI) techniques were assessed for their ability to produce accurate models for predicting a synthetic GR log using surface drilling parameters. These techniques were artificial neural networks (ANN), adaptive network-based fuzzy logic (ANFIS), and functional networks (FN). A total of 4609 data entries from three wells in the Middle East were used to train and test the models and later validate them. The data from wells 1 and 2 were used to develop the AI models, while well 3 was used to validate these models. The obtained results showed the accuracy of predicting the GR using ANN and ANFIS. The correlation coefficient (CC) values of the prediction and measured GR were 0.96, 0.96, and 0.83 for ANN, ANFIS, and FN, respectively. Also, the average absolute percentage error (AAPE) values for the three models in the same order were 3.69%, 3.72%, and 6.7% which shows slightly less accuracy of the FN model. For the validation data set, the ANN and ANFIS models were able to capture all changes in the GR log trend with depth and accurately predict the GR values. A new empirical correlation for GR prediction was developed based on the weight and biases of the optimized ANN model. The new correlation can be used to predict the GR log with high accuracy as a function of the drilling surface data with no need to run AI methods. • Real-time prediction of GR log using surface drilling parameters using AI. • New GR empirical correlation using surface drilling parameters. • ANN and ANFIS models showed a superior performance for GR prediction. [ABSTRACT FROM AUTHOR]

The rheological properties of the drilling fluid are crucial to the success of the drilling project. The traditional mud experiments normally performed by the mud engineers provide rheological data with a small resolution. Monitoring higher-resolution rheological properties is particularly important for all-oil mud because it is widely used with problematic drilled formations. The design and monitoring of the drilling fluid rheology is a critical issue for drilling, and therefore, this paper is a contribution to the effort to completely automate the process of highly accurate and real-time recording of the rheological mud properties. This paper aims to develop intelligent predictive models for the mud rheological properties using artificial neural networks [ANN] by linking the high-frequency mud parameters such as fluid density or mud weight [MWT] and Marsh funnel viscosity [MFV] with the rheological measurements of low frequency for drilling mud such as plastic viscosity [PV], yield point [YP], behavior indicator [n] and viscosity appearance [AV]. New empirical correlations have additionally been established to assess the rheological properties of water. In order to construct ANN models, data was obtained from 56 different wells during drilling operations of different drilling sections with various sizes. The data was fairly enough for building and testing the models as 369 data points were obtained. The models were optimized by trainlm which was the best training function and tansig was the best transfer function. 42 neurons in the hidden layer optimized AV and PV models where 34 neurons optimized all other rheological models [YP, n, R300, and R600]. ANN models presented good results as correlation coefficient [R] was 0.9 and an average absolute [AAPE] error of less than 8% for training and testing data sets. The new models were used to derive the empirical correlations for the estimation of rheological parameters. The empirical correlations were extracted to easily monitor the rheological properties of an all-oil mud system in real-time, which enables better control of the drilling activity by maintaining rheological properties at optimal values as well as early detection of other problems that might require immediate interactions, including well control and stuck pipe. • New models by artificial neural networks for prediction of rheological parameters. [ABSTRACT FROM AUTHOR]

During the drilling operations, drilling fluids invade the drilled formations which could cause a plugging or permeability alteration in the reservoir zone and consequently additional cost to the drilling operations. Buildup of a thin impermeable layer toward a well's wall called the filter cake reduces this invasion. However, this layer must be removed afterward for a better cementing job and to eliminate any restrictions on hydrocarbon flow at the production stage. Filter cake layer composed mainly of particles of the weighting material, in this paper we highlighted the main challenges for water-based filter cake removal which contains barite, ilmenite, manganese tetroxide, and calcium carbonate as a weighting material. Many of the reported filter cake removal processes are multi-stage events because of the incompatibility of the polymer breaker with the removal solution, therefore, more researches need to be conducted to come up with an enzyme compatible with the removal fluids. We also recommend conducting more researches to investigate the effect of the presence of the carbonate or sandstone particles inside the filter cake formation on the removal efficiency and to study the removal performance at high temperatures. • Water-based filter cake removal. • Effect of different weighting materials on the removal process. • Number of stages required of efficient filter cake removal. • Effect of drilled cuttings on the removal efficiency. [ABSTRACT FROM AUTHOR]

In drilling operations, the fine drilled cutting are mixed with the drilling fluid. As a result, the properties of the drilling fluid, such as density and rheological properties, will change based on the cutting properties. Previous studies investigated the effect of rock type with different loadings on the drilling fluid properties. However, the effect of cutting's mechanical properties variation was not addressed. In this paper, the effect of variation in cutting's mechanical properties on the drilling fluid properties was investigated in sandstone reservoir. Particles of sandstone formation core samples with different compressive strength (UCS) and different Young's modulus (E) were mixed, in different percentages, with the drilling fluid. The mechanical properties of the cuttings were determined in the laboratory using destructive method. The drilling fluid density and rheological properties were measured using mud balance and 900-Viscometer®, respectively. The results of this work showed that, for sand content less than 10 wt%, the mechanical properties of the drilled cutting have no effect on drilling fluid properties. The effect of the mechanical properties of the drilled cutting becomes more significant for sand content greater than 10 wt%. In this range of sand content (> 10 wt%), for a constant UCS, drilled cutting with a smaller Young's modulus have a higher effect on the drilling fluid rheological properties compared to drilled cutting with a larger Young's modulus. On the other hand, for a constant E, drilled cutting with larger UCS showed a higher effect on the drilling fluid rheological properties compared to drilled cutting with smaller UCS values. [ABSTRACT FROM AUTHOR]

Abstract Recently, exploration operations for gas wells target deep reservoirs, where the temperature and pressure are very high. High temperature (more than 200 °C) and pressure (more than 5500 psi) result in degrading the complex organic molecules into dry gas or gas condensate. During gas production the reservoir pressure will decrease below the gas dew point and form a condensate bank near to the borehole. Condensate banking has been branded to cause serious drop in the gas effective permeability, gas productivity and results in formation damage. Numerous techniques have been adapted to mitigate gas condensate banking effects in gas wells. These approaches include the injection of solvents and chemicals to alter formation wettability to minimize condensate blockage. Other methods include acids injection, fracturing treatments, and drilling horizontal wells. These methods will lower the rate of pressure drop and permit the production of a single phase gas. This paper critically reviews the causes and instants of condensate banking in gas reservoirs. The developed models that capture and enumerate this phenomenon, and the most adapted mitigation techniques in accordance to the leading conditions are thoroughly discussed. The most successful agents for enhancing condensate production are reported and their effects on the condensate behavior is identified. Furthermore, novel and new technique for permanent condensate removal is presented in this paper. This review will fill the gap in the area of condensate removal treatments by critically analyzing and summarizing the techniques proposed in the literature. Highlights • Review of different condensate removal techniques. • Classify the removal methods to temporary (short term) and long term removal techniques. • Recommend the condensate removal method based on the reservoir conditions and reservoir rock type. • Develop a new permanent condensate removal method using thermochemical fluid injection. [ABSTRACT FROM AUTHOR]

For the purpose of carbon capture, utilization, and storage, carbon dioxide (CO 2) injection in coal formations can enhance methane recovery and mitigate climate change. However, measuring CO 2 adsorption isotherms using experimental or mathematical models can be time-consuming, expensive, and inaccurate. Thus, this study presents a machine-learning framework that predicts CO 2 adsorption in coal formations based on various coal properties and testing conditions. Machine-learning (ML) framework was applied using a dataset of 1,064 points collected for different coal samples at different operating conditions to predict the CO 2 adsorption in coal surface. The ML techniques include decision tree regression (DT), random forests (RF), gradient boost regression (GBR), K-nearest neighbor (KNN), artificial neural network (ANN), function network (FN), and adaptive neuro-fuzzy inference system (ANFIS). The applied framework determines CO 2 adsorption as a function of coal's physical and chemical properties (moisture, ash, volatile matter, and fixed carbon content), the vitrinite reflectance of the coal samples, and testing conditions (pressure and temperature). Classical statical tools such as R2, root mean square error (RMSE), and average absolute percentage error (AAPE) were used to evaluate the model's performance analysis. The results demonstrated the ability to determine CO 2 adsorption for varying coal types and at different temperature and pressure conditions. The statistical measures suggested that RF, GBR, and KNN are very reliable ML models, with RF being the best. At low operating pressure (P < 4 MPa), CO 2 adsorption is impacted by any pressure changes, while it is stabilized at high-pressure values and becomes more dependent on the rock properties at high operating pressure (P > 4 MPa). The introduced ML framework offers a technique to evaluate the capability of different algorithms and accurately estimate CO 2 adsorption without the requirement of additional experimental measurements or complicated mathematical techniques. [ABSTRACT FROM AUTHOR]

Drilling fluids must be cautiously designed to form an impermeable filter cake layer that diminishes the intensity of solid and mud invasion during drilling of a well. Inability to abate solid and mud invasion can cause adverse effects on the productivity of the drilled well. As a result, a variety of additives have been proposed for improving filter cake's sealing properties, which are also utilized to control fluid filtration. This work investigates the role an amorphous volcanic glass (perlite), a novel filtration agent, to amend the quality of filter cake layer that formed using ilmenite (FeTiO 3) drilling fluid. The characteristics of the filter cake were evaluated in terms of the filtration behavior, filter cake thickness, permeability and porosity. In order to accomplish this aim, several concentrations of perlite particles were loaded to the Ilmenite weighted drilling fluid. The tests were conducted under high-pressure high-temperature environment according to API standards. The results of the drilling fluid (containing perlite) were compared with a reference drilling fluid (zero concentration of perlite). It is observed that the perlite containing formulation has improved filtration properties. The flake-shaped perlite particles blocked the filter cake pores and accordingly produced an impermeable filter cake. There was a drastic drop in the filter cake overall permeability after adding perlite concentration of 3 lb/bbl, from 6.09 × 10−3 Darcy to 1.46 × 10−3 Darcy and a significant drop (55%) in the associated filtration volume from 18.5 cm3 to 8.5 cm3. Moreover, perlite-based filter cake lowered the pore size of the filter cake and decreased the filter cake thickness by 45%. [Display omitted] • Improving the filtration properties of Ilmenite-WBM. • Condensed the filter cake thickness of Ilmenite-WBM by 45%. • Fluid loss volume of perlite containing Ilmenite-WBM is reduced by 55%. • The recommended concentration of perlite particles to be added to Ilmenite WBM. [ABSTRACT FROM AUTHOR]

Cementing is a significant operation that has important functions for supporting the oil/gas wells life. Many additives had been added to enhance the cement layer properties towards promising to achieve its functions, especially for the high-temperature conditions. The purpose of this work is to investigate the prospect of utilizing perlite particles (PP) for the cementing process under high-temperature of 292 °F and high-pressure of 3000 psi. Four cement samples containing different concentrations of perlite particles (PP) (0, 1, 2, 3% By Weight of Cement - BWOC) were prepared and compared. The influences of perlite particles (PP) on the cement properties such as thermal conductivity, density variation rheological properties, failure parameters, elastic properties, and petrophysical properties were examined. Nuclear magnetic resonance (NMR) was used to evaluate the porosity distribution for the cement after adding PP. The results showed that the optimum concentration of the perlite particles was 3% BWOC. Perlite material was talented to stabilize the density distribution and significantly reduce the thermal conductivity of the cement layer by 40% compared with base cement (i.e., 0% PP). NMR measurements showed that the deviation in the cement porosity between different sections (top, middle, and bottom) can be effectively controlled by adding perlite particles. Moreover, adding PP to class G cement showed extreme improvement in its rheological properties. Other properties including, tensile strength, compressive strength, Young's modulus were also controlled using containing perlite cement. Image 1 • Assessment of using perlite powder as a thermal isolator for oil-well cementing. • Enhancing the cement homogeneity using perlite powder. • Perlite powder as a rheology modifier for oil-well cementing. • A new approach of evaluating the pore size distribution of heavyweight cement using NMR. [ABSTRACT FROM AUTHOR]