Improving Water-Based Drilling Mud Performance Using Biopolymer Gum: Integrating Experimental and Machine Learning Techniques.

Drilling through shale formations can be expensive and time-consuming due to the instability of the wellbore. Further, there is a need to develop inhibitors that are environmentally friendly. Our study discovered a cost-effective solution to this problem using Gum Arabic (ArG). We evaluated the inhibition potential of an ArG clay swelling inhibitor and fluid loss controller in water-based mud (WBM) by conducting a linear swelling test, capillary suction timer test, and zeta potential, fluid loss, and rheology tests. Our results displayed a significant reduction in linear swelling of bentonite clay (Na-Ben) by up to 36.1% at a concentration of 1.0 wt. % ArG. The capillary suction timer (CST) showed that capillary suction time also increased with the increase in the concentration of ArG, which indicates the fluid-loss-controlling potential of ArG. Adding ArG to the drilling mud prominently decreased fluid loss by up to 50%. Further, ArG reduced the shear stresses of the base mud, showing its inhibition and friction-reducing effect. These findings suggest that ArG is a strong candidate for an alternate green swelling inhibitor and fluid loss controller in WBM. Introducing this new green additive could significantly reduce non-productive time and costs associated with wellbore instability while drilling. Further, a dynamic linear swelling model, based on machine learning (ML), was created to forecast the linear swelling capacity of clay samples treated with ArG. The ML model proposed demonstrates exceptional accuracy (R2 score = 0.998 on testing) in predicting the swelling properties of ArG in drilling mud.

Sand consolidation using enzyme-induced carbonate precipitation: new insights on temperature and particle size effects.

Sand production is a major issue in the oil and gas industry. Unconsolidated sand can be produced with the oil or gas a cause many issues to the production facilities. Enzyme-induced carbonate precipitation (EICP) is a promising method for sand consolidation and is characterized by its environment friendliness. Numerous studies have shown its effectiveness in ambient conditions. However, oil and gas downhole well operations are high pressure and high-temperature conditions. The objective of this study is to investigate effect of high temperature on EICP reaction and its efficiency in terms of uniformity to consolidate different types of sand samples. In this paper, the behavior of EICP solutions is examined in high temperatures from 25 to 90 °C. The study shows that high temperature environment doesn't handicap efficiency but in contrast it can favor the reaction if optimum concentration of reactants has been selected. The temperature effect is also discussed in terms of controllability of reaction which can favor application of reaction. Qualitive analysis shows when EICP solutions containing more than 50,000 ppm of metal ions and stoichiometrically surplus urea requires exposure to heat for reaction progress. The effect of sand particle size and its implication on the consolidation process was examined. Particle size of fine and medium sand ranged from 125 to 250 µm and 250 to 425 µm respectively while for coarse sand 70% sand particle size was between 425 and 700 µm. Designed EICP solutions achieve 9,000 psi for medium and almost 5,000 psi intrinsic specific energy for coarse sand samples. However, treated samples were subject to non-uniform distribution of strength of which can be up to 8,000 psi difference between top and bottom half of the samples.

Optimization of calcium carbonate precipitation during alpha-amylase enzyme-induced calcite precipitation (EICP).

The sand production during oil and gas extraction poses a severe challenge to the oil and gas companies as it causes erosion of pipelines and valves, damages the pumps, and ultimately decreases production. There are several solutions implemented to contain sand production including chemical and mechanical means. In recent times, extensive work has been done in geotechnical engineering on the application of enzyme-induced calcite precipitation (EICP) techniques for consolidating and increasing the shear strength of sandy soil. In this technique, calcite is precipitated in the loose sand through enzymatic activity to provide stiffness and strength to the loose sand. In this research, we investigated the process of EICP using a new enzyme named alpha-amylase. Different parameters were investigated to get the maximum calcite precipitation. The investigated parameters include enzyme concentration, enzyme volume, calcium chloride (CaCl2) concentration, temperature, the synergistic impact of magnesium chloride (MgCl2) and CaCl2, Xanthan Gum, and solution pH. The generated precipitate characteristics were evaluated using a variety of methods, including Thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). It was observed that the pH, temperature, and concentrations of salts significantly impact the precipitation. The precipitation was observed to be enzyme concentration-dependent and increase with an increase in enzyme concentration as long as a high salt concentration was available. Adding more volume of enzyme brought a slight change in precipitation% due to excessive enzymes with little or no substrate available. The optimum precipitation (87%) was yielded at 12 pH and with 2.5 g/L of Xanthan Gum as a stabilizer at a temperature of 75°C. The synergistic effect of both CaCl2 and MgCl2 yielded the highest CaCO3 precipitation (32.2%) at (0.6:0.4) molar ratio. The findings of this research exhibited the significant advantages and insights of alpha-amylase enzyme in EICP, enabling further investigation of two precipitation mechanisms (calcite precipitation and dolomite precipitation).

Productivity Index Prediction for Single-Lateral and Multilateral Oil Horizontal Wells Using Machine Learning Techniques.

Horizontal wells are geometrically shaped differently and projected to different flow regimes than vertical wells. Therefore, the existing laws that govern flow and productivity in vertical wells are not applicable to horizontal wells directly. The objective of this paper is to develop machine learning models that predict well productivity index using several reservoir and well inputs. Six models were developed using the actual well rate data from several wells divided into single-lateral wells, multilateral wells, and a combination of single-lateral and multilateral wells. The models are generated using artificial neural networks and fuzzy logic. The inputs used to create the models are the typical inputs used in the correlations and are well-known for any well under production. The results of the established ML models were excellent as suggested by an error analysis performed, reflecting the models to be robust. The error analysis showed high correlation coefficient values (between 0.94 and 0.95) supported by a low estimation error for four models out of six. The added value of this study is the developed general and accurate PI estimation model that overcomes many limitations of several widely used correlations in the industry and can be utilized for single-lateral or multilateral wells.

Laboratory dielectric measurements to evaluate the conductivity change in the presence of chelating agent with different brines.

In the oil and gas reservoirs, the interaction between the injected fluids and the reservoir fluids and rocks plays a major role in the productivity of any oil and gas field. Studying the ion exchange between reservoir fluids and the injected fluids for water flooding or chemical enhanced oil recovery purposes would help in optimizing the oil displacement process and hence the productivity form such secondary or tertiary recovery mechanisms. Chelating agents are used for enhance oil recovery to improve the oil displacement and sweep efficiency by altering the reservoir rock's surface. When it comes to fluid-rock interaction, conductivity and ionic activity of the injected water will have a great impact on the rock's surface charge and therefore in the reservoir's wettability. Dielectric laboratory measurements have the ability to observe the change in conductivity at high frequency due to the presence of free ions and salts in fluids. In this work, observing the effect of chelating agent with different salts on high frequency conductivity using laboratory dielectric measurements has been conducted. Introducing laboratory dielectric measurement could be a valuable tool in the lab as an evaluation technique into the ion exchange that occurs between different fluids from the reservoir with different brines and additives to study the fluid-fluid interaction activities. It can be also utilized to investigate the maximum chelating capacity of different chelating agents with different cations which can be reflected by the change in conductivity.

Review of Kerogen's Geomechanical Properties: Experiments and Molecular Simulation.

Measuring the mechanical properties of kerogen, the predominant constituent of organic matter in shale is exceedingly difficult as it constitutes small-scale aggregates interspersed in rocks. Kerogen is characterized by significantly lower stiffness compared to inorganic minerals, thereby the kerogen regions are potential areas for study during, for example, drilling or macroscopic fracture propagation in the course of hydraulic fracturing. For instance, the elastic modulus of kerogen-rich spots is around 10 GPa, while it is about 70 GPa for quartz. Failure of the kerogen nanocantilever beam shows an elastic strain-hardening behavior, indicating a higher energy requirement to propagate a crack. Studies illustrated that the kerogen's mechanical properties are controlled by maceral composition and are positively correlated to the maturity level. This paper provides a comprehensive review of how the mechanical properties of kerogen are elucidated experimentally and contrast the results with the properties delineated from molecular simulation. In addition, we relate kerogen innate attributes, such as maturity and type, to the physical qualities measured and substantiate why accurate knowledge of the mechanical characteristics is pivotal from a hydraulic fracturing perspective.

A Review of Enzyme-Induced Calcium Carbonate Precipitation Applicability in the Oil and Gas Industry.

Enzyme-induced calcium carbonate precipitation (EICP) techniques are used in several disciplines and for a wide range of applications. In the oil and gas industry, EICP is a relatively new technique and is actively used for enhanced oil recovery applications, removal of undesired chemicals and generating desired chemicals in situ, and plugging of fractures, lost circulation, and sand consolidation. Many oil- and gas-bearing formations encounter the problem of the flow of sand grains into the wellbore along with the reservoir fluids. This study offers a detailed review of sand consolidation using EICP to solve and prevent sand production issues in oil and gas wells. Interest in bio-cementation techniques has gained a sharp increase recently due to their sustainable and environmentally friendly nature. An overview of the factors affecting the EICP technique is discussed with an emphasis on the in situ reactions, leading to sand consolidation. Furthermore, this study provides a guideline to assess sand consolidation performance and the applicability of EICP to mitigate sand production issues in oil and gas wells.

Application of Machine Learning to Predict Estimated Ultimate Recovery for Multistage Hydraulically Fractured Wells in Niobrara Shale Formation.

The completion design of multistage hydraulic fractured wells including the cluster spacing injected proppant and slurry volumes has shown a great influence on the well production rates and estimated ultimate recovery (EUR). EUR estimation is a critical process to evaluate the well profitability. This study proposes the use of different machine learning techniques to predict the EUR as a function of the completion design including the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations. A data set of 200 well production data and completion designs was collected from oil production wells in the Niobrara shale formation. Artificial neural network (ANN) and random forest (RF) techniques were implemented to predict EUR from the completion design. The results showed a low accuracy of direct prediction of the EUR from the completion design. Hence, an intermediate step of estimating the initial well production rate (Q i ) from the completion data was carried out, and then, the Q i and the completion design were used as input parameters to predict the EUR. The ANN and RF models accurately predicted the EUR from the completion design data and the estimated Q i . The correlation coefficient (R) values between actual EUR and predicted EUR from the ANN model were 0.96 and 0.95 compared with 0.99 and 0.95 from the RF model for training and testing, respectively. A new correlation was developed based on the weight and biases from the optimized ANN model with an R value of 0.95. This study provides ML application with an empirical correlation to predict the EUR from the completion design parameters at an early time without the need for complex numerical simulation analysis. The developed models require only the initial flow rate along with the completion design to predict EUR with high certainty without the need for several months of production similar to the DCA models.

Single step calcium sulfate scale removal at high temperature using tetrapotassium ethylenediaminetetraacetate with potassium carbonate.

Calcium sulfate (CaSO4) scale has been identified as one of the most common scales contributing to several serious operating problems in oil and gas wells and water injectors. Removing this scale is considered an economically feasible process in most cases as it enhances the productivity of wells and prevents potential severe equipment damage. In this study, a single-step method utilizing potassium carbonate and tetrapotassium ethylenediaminetetraacetate (K4-EDTA) at high temperature (200 °F) has been used to remove CaSO4 scale. The CaSO4 scale was converted to calcium carbonate (CaCO3) and potassium sulfate (K2SO4) using a conversion agent, potassium carbonate (K2CO3), at a high temperature (200 °F) and under various pH conditions. Various parameters were investigated to obtain a dissolver composition at which the optimum dissolution efficiency is achieved including the effect of dissolver pH, soaking time, the concentration of K4-EDTA, the concentration of potassium carbonate (K2CO3), temperature impact and agitation effect. Fourier transform infrared, X-ray crystallography, ion chromatography, stability tests and corrosion tests were carried out to test the end product of the process and showcase the stability of the dissolver at high temperature conditions. A reaction product (K2SO4) was obtained in most of the tests with different quantities and was soluble in both water and HCl. It was observed that the dissolver solution was effective at low pH (7) and resulted in a negligible amount of reaction product with 3 wt% CaSO4 dissolution. The 10.5-pH dissolver was effective in most of the cases and provided highest dissolution efficiency. The reaction product has been characterized and showed it is not corrosive. Both 7-pH and 10.5-pH dissolvers showed high stability at high temperature and minimum corrosion rates. The single step dissolution process showed its effectiveness and could potentially save significant pumping time if implemented in operation.

Optimum Selection of H2S Scavenger in Light-Weight and Heavy-Weight Water-Based Drilling Fluids.

During hydrocarbon drilling operations, the presence of hydrogen sulfide (H2S) gas could cause serious health and safety issues. Scavenging this gas and eliminating its impact are essential requirements for a safe drilling operation. This study investigated the impact of three H2S scavenger additives (copper nitrate, iron gluconate, and potassium permanganate) on water-based drilling fluids (WBDFs). The additives were tested on two actual field drilling mud samples that differ mainly in their weight. The scavengers' impact on drilling muds was investigated by measuring their scavenging capacity and their effect on rheology, fluid loss, and pH. Potassium permanganate outperformed the other scavengers when added to the lighter (lower density) WBDF. However, it did not impact the scavenging capacity of the heavier mud system. Copper nitrate outperformed the other scavengers in the heavier drilling mud system. Also, the addition of copper nitrate in the lighter mud system increased its H2S-scavenging capacity greatly, while for iron gluconate, it did not perform very well. Overall, all the scavenger-containing drilling muds did not have any significant harmful impact on the plastic viscosity or the fluid loss properties of the drilling muds. Furthermore, all the tested drilling mud samples showed an excellent ability to clean wellbores and suspend drill cuttings evident by the high carrying capacity with the exception of iron gluconate or potassium permanganate with the heavy mud system.

Experimental Investigation of the Rheological Behavior of an Oil-Based Drilling Fluid with Rheology Modifier and Oil Wetter Additives.

Drilling issues such as shale hydration, high-temperature tolerance, torque and drag are often resolved by applying an appropriate drilling fluid formulation. Oil-based drilling fluid (OBDF) formulations are usually composed of emulsifiers, lime, brine, viscosifier, fluid loss controller and weighting agent. These additives sometimes outperform in extended exposure to high pressure high temperature (HPHT) conditions encountered in deep wells, resulting in weighting material segregation, high fluid loss, poor rheology and poor emulsion stability. In this study, two additives, oil wetter and rheology modifier were incorporated into the OBDF and their performance was investigated by conducting rheology, fluid loss, zeta potential and emulsion stability tests before and after hot rolling at 16 h and 32 h. Extending the hot rolling period beyond what is commonly used in this type of experiment is necessary to ensure the fluid's stability. It was found that HPHT hot rolling affected the properties of drilling fluids by decreasing the rheology parameters and emulsion stability with the increase in the hot rolling time to 32 h. Also, the fluid loss additive's performance degraded as rolling temperature and time increased. Adding oil wetter and rheology modifier additives resulted in a slight loss of rheological profile after 32 h and maintained flat rheology profile. The emulsion stability was slightly decreased and stayed close to the recommended value (400 V). The fluid loss was controlled by optimizing the concentration of fluid loss additive and oil wetter. The presence of oil wetter improved the carrying capacity of drilling fluids and prevented the barite sag problem. The zeta potential test confirmed that the oil wetter converted the surface of barite from water to oil and improved its dispersion in the oil.