Abstract:
Damage to a casing string in a wellbore resulting from a wellbore operation can be predicted. For example, a stiff string model can be used to determine a contact point between the casing string and a well tool positionable within the casing string for performing the wellbore operation. The stiff string model can be used to determine a force of the well tool against the casing string at the contact point. The force can be used to determine a volume of damage to the casing string proximate to the contact point. A depth of a groove formed in the casing string proximate to the contact point can be determined based on the volume of damage.
Abstract:
Drilling system and methods may employ a weight-on-bit optimization for an existing drilling mode and, upon transitioning to a different drilling mode, determine an initial weight-on-bit within a range derived from: a sinusoidal buckling ratio, a helical buckling ratio, and the weight-on-bit value for the prior drilling mode. The sinusoidal buckling ratio is the ratio of a minimum weight-on-bit to induce sinusoidal buckling in a sliding mode to a minimum weight-on-bit to induce sinusoidal buckling in a rotating mode, and the helical buckling ratio is the ratio of a minimum weight-on-bit to induce helical buckling in the sliding mode to a minimum weight-on-bit to induce helical buckling in the rotating mode. The ratios are a function of the length of the drill string and hence vary with the position of the drill bit along the borehole.
Abstract:
Systems and methods for well integrity management in all phases of development using a coupled engineering analysis to calculate a safety factor, based on actual and/or average values of various well integrity parameters from continuous real-time monitoring, which is compared to a respective threshold limit.
Abstract:
Predicting casing wear, riser wear, and friction factors in drilling operations may be achieved with data-driven models that use discrete inversion techniques to updated casing wear models, riser wear models, and/or friction factor models. For example, a method may applying a linear inversion technique or a nonlinear inversion technique to one or more parameters of at least one of a casing wear model, a riser wear model, or a friction factor model using historical data from a previously drilled well as input data to produce at least one of an updated casing wear model, an updated riser wear model, or an updated friction factor model, respectively; and implementing the at least one of the updated casing wear model, the updated riser wear model, or the updated friction factor model when designing and/or performing a drilling operation.
Abstract:
Estimating casing wear during a reciprocation portion of a drilling operation may take into account the forces that cause casing wear during the up and down strokes independently. For example, during a drilling operation, a method may include reciprocating the drill string through the wellbore for a plurality of up strokes and a plurality of down strokes according to reciprocation parameters; calculating an up stroke normal force and a down stroke normal force for the casing or a section thereof; calculating up and down stroke casing wears based on the up and down stroke normal forces, respectively, using a reciprocation casing wear model; calculating a reciprocation casing wear based on the up and down stroke casing wears; and calculating a total casing wear for the casing or the section thereof based on the reciprocation casing wear using a total casing wear model.
Abstract:
A method including obtaining input attribute values and a target risk attribute value associated with a first borehole segment. The method also includes training a prediction model for the target risk attribute using the input attribute values and the target risk attribute value. The method also includes acquiring subsequent input attribute values. The method also includes using the trained prediction model and the subsequent input attribute values to predict a target risk attribute value for a second borehole segment. The method also includes storing or displaying information based on the predicted target risk attribute value.
Abstract:
Disclosed embodiments include a method for estimating casing wear including the operations of: obtaining locations of survey points along a borehole, said survey point locations representing a borehole trajectory; casing at least a portion of the borehole with a casing string; deriving locations of adjusted survey points that represent a casing trajectory along said portion of the borehole, the casing trajectory being different from the borehole trajectory; estimating, as a function of position along said casing string, a side force of a drill string against the casing string; computing, as a function of position along the casing string, casing wear based at least in part on the side force; and generating a notification of any positions where casing wear exceeds a threshold.
Abstract:
Estimating casing wear for individual portions or lengths of a casing may take into account that individual drill string sections cause different amounts of casing wear based on the physical and material properties of each drill string section. In some instances, a method performed during a drilling operation may involve tracking a location of the plurality of drill string sections along the wellbore; corresponding a casing section with the drill string wear factors of the drill string sections radially proximate to the casing section the drilling intervals of the drilling operations; and calculating a drilling casing wear for the casing section based on the drill string wear factors corresponding to the casing section.
Abstract:
Systems and methods for well integrity management in all phases of development using a coupled engineering analysis to calculate a safety factor, based on actual and/or average values of various well integrity parameters from continuous real-time monitoring, which is compared to a respective threshold limit.
Abstract:
A method including obtaining input attribute values and a target risk attribute value associated with a first borehole segment. The method also includes training a prediction model for the target risk attribute using the input attribute values and the target risk attribute value. The method also includes acquiring subsequent input attribute values. The method also includes using the trained prediction model and the subsequent input attribute values to predict a target risk attribute value for a second borehole segment. The method also includes storing or displaying information based on the predicted target risk attribute value.