No abstract

In deep hydrocarbon development wells, cement slurry with high density is required to effectively balance the high-pressure formations. The increase in the slurry density could be achieved by adding different heavy materials. In this study, the effect of the weighting materials (barite, hematite, and ilmenite) on the properties of Saudi Class G cement matrix of vertical homogeneity, compressive strength, porosity, and permeability was evaluated. Three cement slurries were weighted with barite, hematite, and ilmenite, and cured at 294 °F and 3000 psi for 24 h. All slurries have the same concentration of the different additives except the weighting material. The amount of weighting material used in every slurry was determined based on the targeted density of 18 lbm/gal. The results of this study revealed that the most vertically homogenous cement matrix was the ilmenite-weighted sample with a vertical variation of 17.6% compared to 20.2 and 24.8% for hematite- and barite-weighted cement, respectively. This is attributed to the small particle size of the ilmenite. The medical computerized tomography (CT) scan confirmed that the ilmenite-weighted sample is the most homogeneous, with a narrow range of density variation vertically along the sample. Hematite-weighted cement showed the highest compressive strength of 55.3 MPa, and the barite- and ilmenite-weighted cement compressive strengths are each 18.4 and 36.7% less than the compressive strength of the hematite-weighted cement, respectively. Barite-weighted cement has the lowest porosity and permeability of 6.1% and 18.9 mD, respectively. The maximum particle size of ilmenite used in this study is less than 42 μm to ensure no abrasion effect on the drilling system, and it minimized the solids segregation while maintaining a compressive strength that is higher than the minimum acceptable strength, which is the recommended weighting material for Saudi Class G cement.

No abstract

The standard torque and drag (T&D) modeling programs have been extensively used in the oil and gas industry to predict and monitor the T&D forces. In the majority of cases there has been variability in the accuracy between the pre-calculated (based on a T&D model) and actual T&D values, because of the dependence of the model's predictability on guessed inputs (matching parameters) which may not be correctly predicted. Therefore, to have a reliable model, program users must alter the model inputs, and mainly the friction coefficient to match the actual T&D. This, however, can conceal downhole conditions such as cutting beds, tight holes and sticking tendencies. The objective of this study is to develop an intelligent machine to predict the continuous profile of the surface drilling torque to enable detection of operational problems ahead of time. This paper details the development and evaluation of an intelligent system which could promote safer operation and extend the response time limit to prevent undesired events. Actual field data of Well-1, starting from the time of drilling a 5-7/8-inch horizontal section until one day prior to the stuck pipe incident, was used to train and test three models: random forest, artificial neural network, and functional network, with an 80/20 training-to-testing data ratio, to predict the surface drilling torque. The independent variables for the model are the drilling surface parameters, namely: flow rate (Q), hook load (HL), rate of penetration (ROP), rotary speed (RS), standpipe pressure (SPP), and weight-on-bit (WOB). The prediction capability of the models was evaluated in terms of correlation of coefficient (R) and average absolute error percentage (AAPE). The model with the highest R and lowest AAPE was selected to continue with the analysis to detect downhole abnormalities. The best developed model was used to predict the surface drilling torque in the last day leading up to the incident in Well-1, which represents the normal and healthy trend. Then the model was coupled with a multivariate metric distance called “Mahalanobis” to be used as a classification tool to measure how close an actual observation is to the predictive normal and healthy trend. Based on a pre-determined threshold, each actual observation was labeled “NORMAL” or “ANOMAL”. Well-2 with a stuck pipe incident was used to assess the capability of the developed system in detecting downhole abnormalities. The results showed that in Well-1, where a stuck pipe incident was reported, a continuous alarm was detected by the developed system nine hours before the drilling crew observed any abnormality, while the alarm was detected seven hours prior to any observation by the crew in Well-2. The developed intelligent system could help the drilling crew to detect downhole abnormalities in real time, react and take corrective action to mitigate the problem promptly.