Faster production declines than initially forecast were observed in numerous deep-water assets. These wells were completed as Cased Hole Frac-Pack (CHFP) completions (
Seven key damage mechanisms were identified as forming the basis for PI degradation: 1) off-plane perforation stability, 2) fines migration, 3) fracture conductivity, 4) fracture connectivity, 5) fluid invasion, 6) non-Darcy flow and 7) creep effects. A near wellbore production model incorporating the completion, fracture geometry and reservoir is coupled with a geomechanics model to assess each mechanism. A Design of Experiment setup varies the input ranges associated with each of the seven damage mechanisms. Input parameters for the model are risked and rely on ranges from standard and newly developed well and lab tests. The model assesses well performance and driving mechanisms at different points in time within the production life.
Primarily the study focused on high permeability and highly over pressured reservoirs. For the types of wells/fields assessed in the study, the results indicated three phases of decline based on the interaction between the formation properties, the completion components and the operating parameters. The three phases breakdown into: (1) a pre-rock failure stage where declines are relatively small, (2) an ongoing rock failure stage where declines are rapid and (3) a post failure stage where declines are again moderate. In each of these stages different parameters and damage mechanisms were assessed to be impactful. The workflow was also utilized to match pre and post acidizing treatments. A comparison for varying rock types was included looking at the impact of rock strength and formation permeability on the ranking of the damage mechanisms. The impact of operating parameters such as drawdown can also be assessed with the tool showing that increased drawdowns may not always be beneficial to the long-term production of the well.
The paper presents the underlying drivers for PI Decline for deep-water assets of a specific attribute set. Through accurate representation of reservoir and completion, the workflow highlights the impact and combined impact of different damage mechanisms. The paper also shows a direct link between the mechanical properties (moduli and strength) and boundary conditions (pore pressure and stress) and the well performance and productivity. The workflow provides a methodology by which lab and field tests can be transformed into assessments of future well performance without strictly relying on analogs that may or may not be appropriate.
The need for monitoring individual well production in unconventional fields is rising. The drivers are primarily related to accurate reporting for production allocation between wells. The main driver in North American operations for a meter-per-well flow rate monitoring has been the need for accurate per well production accounting due to the complexity of the land-owner interest.
There are additional benefits from the monitoring of early decline and determination of the transient evolution of the reverse productivity index (RPI) to evaluate the well performance. The availability of long-term rate transient data supports decline analysis and rate transient analysis, leading to better understanding of the estimated ultimate recovery (EUR), which may drive the selection of infill drilling locations. Finally, the identification of interference between flowing wells can help mitigate the issues of parent/child wells.
A specific case in the Eagle Ford is the systematic deployment of full gamma-spectroscopy multiphase flowmeters at well pads. This intelligent pad architecture consists of one multiphase flowmeter per well and a production manifold that enables commingling of the production to a single flowline connected to the inlet manifold of the production facility.
The rationale of the decision for the installation of such solution in lieu of a metering separator per well is based on the evaluation of the impact of this technology on capex and opex reductions.
Several lessons learned are provided. They include a discussion of the change management issues related to the installation of the meters, the modifications necessary to the production facility at the receiving side, and the data management and data analytics that were enabled from the gathering of systematic, continuous, and high-resolution measurements.
The impact of the installation of the meters in the field is noticeable and quantifiable. with several prior wells used as a benchmark. The effects are not limited to cost reduction, but also lead to an increase in production related to the release of operational crews from daily well testing tasks that used to be necessary. The data quality and coverage are also increased.
A few suggestions are made concerning optimization of the deployment and use of remote monitoring options for enhanced efficiency. Automated data workflows are also discussed.
The reduction of HSE risks through a better management of field operators is also assessed.
Reliability of subsurface assessment for different field development scenarios depends on how effective the uncertainty in production forecast is quantified. Currently there is a body of work in the literature on different methods to quantify the uncertainty in production forecast. The objective of this paper is to revisit and compare these probabilistic uncertainty quantification techniques through their applications to assisted history matching of a deep-water offshore waterflood field. The paper will address the benefits, limitations, and the best criteria for applicability of each technique.
Three probabilistic history matching techniques commonly practiced in the industry are discussed. These are Design-of-Experiment (DoE) with rejection sampling from proxy, Ensemble Smoother (ES) and Genetic Algorithm (GA). The model used for this study is an offshore waterflood field in Gulf-of-Mexico. Posterior distributions of global subsurface uncertainties (e.g. regional pore volume and oil-water contact) were estimated using each technique conditioned to the injection and production data.
The three probabilistic history matching techniques were applied to a deep-water field with 13 years of production history. The first 8 years of production data was used for the history matching and estimate of the posterior distribution of uncertainty in geologic parameters. While the convergence behavior and shape of the posterior distributions were different, consistent posterior means were obtained from Bayesian workflows such as DoE or ES. In contrast, the application of GA showed differences in posterior distribution of geological uncertainty parameters, especially those that had small sensitivity to the production data. We then conducted production forecast by including infill wells and evaluated the production performance using sample means of posterior geologic uncertainty parameters. The robustness of the solution was examined by performing history matching multiple times using different initial sample points (e.g. random seed). This confirmed that heuristic optimization techniques such as GA were unstable since parameter setup for the optimizer had a large impact on uncertainty characterization and production performance.
This study shows the guideline to obtain the stable solution from the history matching techniques used for different conditions such as number of simulation model realizations and uncertainty parameters, and number of datapoints (e.g. maturity of the reservoir development). These guidelines will greatly help the decision-making process in selection of best development options.
This paper uses pseudo-time to extend the application of constrained multiwell deconvolution algorithm to gas reservoirs with significant pressure depletion. Multiwell deconvolution is the extension of single well deconvolution to multiple interfering wells. Constraints are added to account for a-priori knowledge on the expected deconvolved derivative behaviors and to eliminate non-physical solutions.
Multiwell deconvolution converts pressure and rate histories from interfering wells into constant-rate pressure responses for each well as if it were producing alone in the reservoir. It also extracts the interference responses observed at each of the other wells due to this single well production. The deconvolved responses have the same duration as the pressure history. This allows to identify reservoir features not visible during individual build ups.
Deconvolution techniques can only be applied to pressure and rate data when flow can be represented by linear equations. In strongly depleted gas reservoirs, fluid properties, and gas compressibility in particular, are pressure dependent, which makes the flow problem non-linear. The paper uses pseudo-pressure and pseudo-time transforms to linearize the problem in such conditions.
The pseudo-time method developed by
The paper extends the application of constrained multiwell deconvolution to strongly depleted gas reservoirs. Constrained multiwell deconvolution is an efficient way to exploit data recorded by permanent downhole pressure gauges and provides information not otherwise available. It can help to identify field heterogeneities and compartmentalization early in field life, making it possible to modify the field development plan and to improve locations of future wells. It can accelerate history-matching with the reservoir model by doing it on the constant rate pressure responses rather than on the actual, usually complex, production history. An added advantage is that comparison between the pressure derivatives of the model and the actual deconvolved derivatives allows identification of mismatch causes.
Severe drilling dynamics of a bottomhole assembly (BHA) causes energy to dissipate into vibrations which undermines drilling efficiency. Dangerous dynamics modes, such as backward whirling and high frequency torsional oscillation, could cause downhole drilling tools to fail prematurely. To mitigate the risk of failure due to these dangerous conditions, it is critical to identify the damaging dynamics modes by interpreting the drilling data. Based on a deep learning approach, a novel method was proposed to automatically identify severe drilling dynamics modes directly from the time-series data.
The drilling dynamics data can be obtained from either a downhole sensor measurement or transient dynamics simulation. First, a deep neural network, which is composed of convolutional and fully connected layers, is employed to explore patterns in the data by generating a feature map of drilling dynamics. Knowledge of drilling dynamics physics can be used to facilitate data clustering in the feature map. Each data cluster can be tagged with the corresponding drilling dynamics mode. Using the tagged dataset, a machine learning classification model can be trained to automatically identify the dynamics modes based on the input of time-series drilling data.
The deep learning approach can be implemented to recognize a collection of dynamics modes of BHA, such as various whirling patterns and high frequency torsional resonance. The most commonly available drilling dynamics data channels, accelerations and collar RPM, were used as the model inputs. The deep neural network was trained to predict the next data sample based on the previous time-series data. One of the hidden layers of the neural network was employed to generate the feature map, in which the dataset forms several clusters. The orbits of BHA movement were plotted on top of the clusters for pattern visualization. After this practice, the simple polygon boundary was drawn between whirling and stable cases, and the dataset was tagged automatically. With the tagged dataset, the classification model was trained to identify various whirling patterns and the stable drilling state. Similar processes can be readily applied to interpret other dynamics modes. Interpreting the drilling dynamics modes provided a high-level description of the data, which offered clues on how to optimize BHA design and drilling practices to improve efficiency.
The automatic interpretation of drilling dynamics data can significantly improve the consistency and efficiency of the existing manual interpretation workflow. The generated feature map enables the exploration of new motion patterns and new vibration modes. This approach eliminates the need to manually tag the data. With minimum human interactions, the dataset can be automatically tagged. The model employs only the raw time series data of basic dynamics channels as inputs, which makes the algorithm universally applicable for various data sources.
I am honored to be recognized at an international level by my peers for my years of service to SPE. My involvement with SPE has greatly expanded my professional network, which has led to some amazing opportunities in my career. Favorite volunteer activity: Perhaps, my favorite endeavor was serving on the organizing committee for an SPE Applied Technology Workshop on Deepwater Operations – Post Drilling & Completions held in 2012. My message to young professionals (YPs): Take an active role in any professional organization to which you belong. I felt grateful to the colleagues and friends who initiated and supported the nomination.
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