This course discusses the fundamental sand control considerations involved in completing a well and introduces the various sand control techniques commonly used across the industry, including standalone screens, gravel packs, high rate water packs and frac-packs. It requires only a basic understanding of oilfield operations and is intended for drilling, completion and production personnel with some sand control experience who are looking to gain a better understanding of each technique’s advantages, limitations and application window for use in their upcoming completions.
This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders.
This course provides a fundamental understanding of process safety techniques and how applying these techniques can improve safety, equipment reliability, environmental performance and reduce overall costs. It presents an overview of the elements comprising process safety, practical examples and how process safety can be integrated into day-to-day operations. Working and studying abroad is a huge part of the oil and gas industry and despite the impact on a professional’s career and personal life, little guidance is available for those considering the big move. At this event, we will be sharing stories from those who have gone through the same process and explore some of the benefits and difficulties of diverse working environments. Sustainability means many different things to different people. For governments, it means ensuring development that meets the needs and aspirations of the present without compromising the ability of future generations to meet their own needs.
Green fields today mostly can be regarded as marginal fields and successfully developed. It covers the complete assessment of the oil and gas recovery potential from reservoir structure and formation evaluation, oil and gas reserve mapping, their uncertainties and risks management, feasible reservoir fluid depletion approaches, and to the construction of integrated production systems for cost effective development of the green fields. Depth conversion of time interpretations is a basic skill set for interpreters. There is no single methodology that is optimal for all cases. Next, appropriate depth methods will be presented. Depth imaging should be considered an integral component of interpretation. If the results derived from depth imaging are intended to mitigate risk, the interpreter must actively guide the process.
Decisions in E&P ventures are affected by Bias, Blindness, and Illusions (BBI) which permeate our analyses, interpretations and decisions. This one-day course examines the influence of these cognitive pitfalls and presents techniques that can be used to mitigate their impact. Bias refers to errors in thinking whereby interpretations and judgments are drawn in an illogical fashion. Blindness is the condition where we fail to see an unexpected event in plain sight. Illusions refer to misleading beliefs based on a false impression of reality.
Uncertainty range in production forecasting gives an introduction to uncertainty analysis in production forecasting, including a PRMS based definition of low, best and high production forecasts. This page topic builds on this with more details of how to approach uncertainty analysis as part of creating production forecasts. Probabilistic subsurface assessments are the norm within the exploration side of the oil and gas industry, both in majors and independents. However, in many companies, the production side is still in transition from single-valued deterministic assessments, sometimes carried out with ad-hoc sensitivity studies, to more-rigorous probabilistic assessments with an auditable trail of assumptions and a statistical underpinning. Reflecting these changes in practices and technology, recently SEC rules for reserves reporting (effective 1 January 2010) were revised, in line with PRMS, to allow for the use of both probabilistic and deterministic methods in addition to allowing reporting of reserves categories other than "proved." This section attempts to present some of the challenges facing probabilistic assessments and present some practical considerations to carry out the assessments effectively. It should be noted that for simplicity the examples referred to in this section are about calculating OOIP rather than generating probabilistic production forecasts directly. Clearly OOIP/GOIP is the starting point of any production forecast and gives a firm basis from which to build production forecasts.
Phase behavior plays an important role in a variety of enhanced oil recovery (EOR) processes. Such processes are designed to overcome, in one way or another, the capillary forces that act to trap oil during waterflooding. Interpretation of phase diagrams is particularly important in the design of surfactant/polymer processes and gas-injection processes. In surfactant/polymer displacement processes, the effects of capillary forces are reduced by injection of surfactant solutions that contain molecules with oil- and water-soluble portions. Such molecules migrate to the oil/water interface and reduce the interfacial tension, thereby reducing the magnitude of the capillary forces that resist movement of trapped oil. Figure 1 shows phase diagrams typical of those used to describe the behavior of surfactant systems.
Garcia, Artur Posenato (The University of Texas at Austin) | Hernandez, Laura M. (The University of Texas at Austin) | Jagadisan, Archana (The University of Texas at Austin) | Heidari, Zoya (The University of Texas at Austin) | Casey, Brian (University Lands) | Williams, Rick (University Lands)
Reliable formation evaluation in organic-rich mudrocks requires integrated interpretation of well logs and core measurements. More than 80% of the Permian Basin wells have incomplete data sets, lacking photoelectric factor (PEF) or other logs, required for reliable formation evaluation in the presence of complex mineralogy. Hence, we develop a novel workflow to reliably estimate rock properties in wells with incomplete data to enhance reservoir characterization and completion decisions. We propose to (a) use integrated rock classification for enhanced physics-based assessment of rock properties in wells with missing data, (b) combine field-scale geostatistical and machine learning methods to reliably reconstruct missing PEF logs with a confidence interval through a rock-type-based approach which is a unique contribution of this work, and (c) quantify the uncertainty in estimates of petrophysical properties.
We performed a preliminary field-scale formation evaluation on wells with triple-combo logs (more than 70 wells). Next, we performed an initial rock typing and reconstructed the missing PEF logs by combining supervised neural networks with geostatistical analysis on a rock-type basis. We then used an unsupervised neural network method to improve the rock classification based on the updated estimates of petrophysical, compositional, and mechanical properties after PEF reconstruction. The combined rock classification and PEF reconstruction was performed iteratively to improve the multi-mineral analysis results in all wells with missing data. We successfully applied the new workflow to 20 wells in blind tests. The reconstructed well logs agreed with the actual measurements with relative errors of less than 10%.
The new workflow extends the boundaries of reliable formation evaluation, enabling accurate reservoir characterization and completion decisions by enhancing evaluation of wells with missing data. This is achieved by reliably reconstructing missing PEF logs with a confidence interval, in a class-by-class basis, which is a unique contribution of this work. The proposed method can be applied to wells with other types of missing data. Analysis of the production data showed that, ceteris paribus, the best rock types obtained from the workflow had approximately 30% higher hydrocarbon production than other rock types.
The results of an investigative research study on the impact of the in-situ stress, shale matrix composition, maturity, amount of organic matter and clay composition affecting the anisotropy level of the geomechanical properties have been discussed in this paper. These parameters are among the key factors known to control the geomechanical properties in organic-rich shale formations. Organic-rich shale formations with different mineralogical compositions and organic matter maturity have been measured under uniaxial and triaxial stress state along with the field data from limited number of the wells in these shale basins where the core samples are obtained to investigate the role of each factor on the level of geomechanical anisotropy.
The field data has been analyzed to compare the trends obtained from the laboratory data collected under customized controlled field conditions to the field data trends. Artificial Neural Network (ANN) analysis was used in wells without full log suits to obtain the anisotropic geomechanical parameters. The results highlight the maturation, organic richness and clay composition effect on the recorded field data as well as the geomechanical properties obtained from the laboratory measurements.
The stress and fluid sensitivity of shale formations have been well recognized since the early days of conventional reservoir drilling, completion and production operations as they typically require special attention for minimizing wellbore instability during drilling and maintaining high integrity wells throughout the life cycle of these wells. Shales are highly heterogeneous and anisotropic formations and their source rock characteristics also have introduced further complexities with the organic matter and compositional variations throughout the areal extent of the reservoirs. These variations and their alterations as a function of the level of maturity of the organic matter require further study for better understanding of the differences and similarities between the seal shales and reservoir shales and the role of the organic matter and its maturity level in these differences. One of the critical aspects of the organic matter presence is in quantification of shale mechanical properties and strength and their direction dependence for successful field development. The level of maturity of the organic matter also influences the mechanical, acoustic, petrophysical and failure properties of organic rich shale formations. The mineralogical composition typically deviates from carbonate rich to quartz rich in the rock matrix with clay and organic matter amount and distribution heterogeneity in the reservoir. The layered structure introduced by the depositional history of the formation along with the heterogeneity in the distribution of organic matter result in various degree of anisotropy in reservoir properties (Sondergeld and Rai, 2011; Vernik and Milovac, 2011). A better understanding on the anisotropic characteristics of the shale formations and key parameters impacting the anisotropy is essential for field operational success from exploration studies for seismic attributes to reservoir characterization, drilling and hydraulic fracture design and production optimization.
Gupta, Ishank (University of Oklahoma) | Devegowda, Deepak (University of Oklahoma) | Jayaram, Vikram (Pioneer Natural Resources) | Rai, Chandra (University of Oklahoma) | Sondergeld, Carl (University of Oklahoma)
Planning and optimizing completion design for hydraulic fracturing require a quantifiable understanding of the spatial distribution of the brittleness of the rock and other geomechanical properties. Eventually, the goal is to maximize the SRV (Stimulated Reservoir Volume) with minimal cost overhead. The compressional and shear velocities (Vp and Vs respectively) can be used to calculate Young’s modulus, Poisson’s ratio and other mechanical properties. In the field, sonic logs are not commonly acquired and operators often resort to regression to predict synthetic sonic logs. We have compared several machine learning regression techniques for their predictive ability to generate synthetic sonic (Vp and Vs) and a brittleness indicator, namely hardness, using the laboratory core data. We used techniques like multi-linear regression, lasso regression, support vector regression, random forest, gradient boosting and alternating conditional expectation. We found that the commonly used multi-linear regression is sub-optimal with less-than-satisfactory predictive accuracies. Other techniques particularly random forest and gradient boosting have greater predictive capabilities based on several error metrics such as R2 (Correlation Coefficient) and RMSE (Root Mean Square Error). We also used Gaussian process simulation for uncertainty quantification as it provides uncertainty estimates on the predicted values for a wide range of inputs. Random Forest and Extreme Gradient Boosting techniques also gave low uncertainties in prediction.