Ilmenite Inclusion: A Solution towards Solid Sagging for Hematite-Based Invert Emulsion Mud

The sagging tendency of hematite in drilling mud is a common challenge occurring at high-pressure and high-temperature (HP/HT) applications. This work studies the performance of hematite-based invert emulsion mud for HP/HT conditions and provides a solution to prevent the hematite settlement using a combination of ilmenite with hematite. Practical mud formulation was utilized over a range of ilmenite/hematite ratios (0/100, 20/80, 40/60, and 50/50%) to study sagging behaviour. From the sag tests, the optimum combination proportion was determined. Thereafter, the density, emulsion stability, rheological and viscoelastic properties, and filtration conduct for the formulated mud were evaluated. The experiments were conditioned as per the standards of the American Petroleum Institute. The obtained results of sagging experiments indicated that including 50% of ilmenite mitigated the hematite settling and reduced the sag tendency towards the safe range. A slight drop (4%) in mud weight was noticed upon adding the ilmenite, whereas the emulsion stability was enhanced from 551 to 574 volts with the 50% ilmenite content. The rheology and viscoelasticity measurements showed that 50/50% combination improved the yield point (YP) by 50% with a trivial 1 cP increment on plastic viscosity (PV), hence enhancing the YP/PV ratio by 46%. Also, the gelling strength was enhanced resulting in flat rheology and better gel structure. The filtration behaviour of 50% ilmenite mud was improved compared to blank hematite as it resulted in 21, 15, and 17% reduction on the filtrated volume, filter cake weight, and thickness, respectively. This study provides a solution for hematite sagging issue at HP/HT using combined weighting agents, which contributes to enhancing the mud stability and avoiding several well control issues and related operational and technical challenges that eventually will economize the drilling cost and time.

Modified Corn Starch as an Environmentally Friendly Rheology Enhancer and Fluid Loss Reducer for Water-Based Drilling Mud

Summary Drilling fluid rheology and fluid loss property are fundamental parameters that dictate the effectiveness and easiness of a drilling operation. Maintaining these parameters under high temperatures is technically challenging and has been an exciting research area for the drilling industry. Nonetheless, the use of drilling mud additives, particularly synthetic polymers, threaten ecological environments. Herein, modified corn starch (MCS) was synthesized, characterized, and investigated as an environmentally friendly rheology enhancer and filtration loss controlling agent for water-based mud (WBM) at high temperatures. The experimental results indicated that MCS exhibits better performance in improving rheological properties and fluid loss controlling ability for WBM than the commonly used mud additives. With the addition of an optimal concentration (0.3 wt%), MCS improved the rheology and fluid loss behavior of WBM formulation at harsh aging temperature (220°C) by practically 4 times and 1.7 times, respectively. The MCS was revealed to perform superbly over polyanionic cellulose (PAC) addition at all investigated temperatures. The better performance of the MCS was ascribed to the improved entanglements in the mud system owing to the additional hydroxyl (OH) groups. Besides, the Herschel-Bulkley model was found to be a constitutive model that described the rheological properties of the investigated muds satisfactorily. Moreover, the MCS was found to exhibit acceptable biodegradability properties.

Carbonate oil and gas source rocks wettability alteration due to influence of polymer-colloidal drilling mud

Evaluation of rock wettability is an important task, since this parameter determines the distribution of water and oil in the reservoirs and their relative and phase permeability. The reliability of evaluation the wettability of rock samples depends on the drilling-in conditions during core sampling and core sample preparation methods. The investigation of the surface properties of the core from the Orenburg oil and gas condensate field showed that using of polymer-colloidal drilling mud leads to hydrophilization of the samples' surface. To obtain information on the actual wettability values of rock samples taken from wells drilled with polymer-colloidal drilling mud a method for estimating the relative (predominant) wettability of rocks based on petrophysical and lithological studies data is proposed. The authors suggest that the extraction of oil and gas source rock samples leads to irreversible changes in surface properties that cannot be restored. Keywords: selective wettability; relative wettability; predominant wettability; polymer-colloidal drilling mud; residual gas saturation; trapped gas saturation; pore space structure; extraction.

Analysis of the deep drilling technology in unstable formations at the Semyrenky gas condensate field

The paper presents a general overview of deep drilling in unstable formations at the Semyrenky gas condensate field of the Dnipro-Donetsk Trough, including well design, bottom hole assemblies (BHA), drilling conditions, and drilling muds. Problems encountered during drilling for production casing of Wells 72- and 75-Semyrenky using high-speed drilling methods are analyzed. The relationships between the rate of penetration and disturbed rock stability, volume excess and depth, as well as consistent empirical patterns in changes in mud properties and depth are established. With these technical and economic performance indicators for well drilling are given, elements of a borehole stability management strategy were defined, the principles of mud selection for drilling through problem zones are validated. The paper discusses the requirements to a mud hydraulics program to reduce the erosion of borehole walls, specific borehole preparation techniques, such as reaming and gauging, for drilling in problem zones, and alternative options to ensure borehole stability. Keywords: borehole stability; statistical models; hole gauging; hole geometry; drilling mud; BHA.

Characterization based machine learning modeling for the prediction of the rheological properties of water-based drilling mud: an experimental study on grass as an environmental friendly additive

The successful drilling operation depends upon the achievement of target drilling attributes within the environmental and economic constraints but this is not possible only on the basis of laboratory testing due to the limitation of time and resources. The chemistry of the mud decides its rheological potential and selection of the techniques required for recycling operations. Conductivity, pH, and photometer testing were performed for the physio-chemical characterization of the grass to be used as an environmental friendly drilling mud additive. In this study, different particle sizes (75, 150, and 300 µm) of grass powder were mixed in mud density of 8.5, 8.6, and 8.7 ppg in the measurement of gel strength and viscosity of drilling mud. The grass additive was added in different weight conditions considering no additive, 0.25, 0.5, and 1 g to assess the contribution of grass on the gel strength and viscosity of the drilling mud. The machine learning techniques (Multivariate Linear Regression Analysis, Artificial Neural Network, Support Vector Machine Regression, k-Nearest Neighbor, Decision Stump, Random Forest, and Random Tree approaches) were applied to the generated rheological data. The results of the study show that grass can be used for the improvement of the gel strength and viscosity of the drilling mud. The highest improvement of the viscosity was seen when grass powder of 150 µm was added in the 8.7 ppg drilling mud in 0.25, 0.5, and 1 g weights. The gel strength of the drilling mud was improved when the grass additive was added to the drilling mud 8.7 ppg. Random forest and Artificial Neural Network had the same results of 0.72 regression coefficient (R2) for the estimation of viscosity of the drilling mud. The random tree was found as the most effective technique for the modeling of gel strength at 10 min (GS_10min) of the drilling mud. The predictions of Artificial Neural Network had 0.92 R2 against the measured gel strength at 10 s (GS_10sec) of the drilling mud. On average, Artificial Neural Network predicted the rheological properties of the mud with the highest accuracy as compared to other machine learning approaches. The work may serve as a key source to estimate the net effect of grass additives for the improvement of the gel strength and viscosity of the drilling mud without the performance of any large number of laboratory tests.