Apache named Gary T. Clark as vice president of investor relations. He joins Apache from Chesapeake Energy, where he was the vice president of investor relations and research. Before joining Chesapeake in 2012, Clark worked as a senior securities analyst at the State Teachers Retirement System of Ohio and as a co-portfolio manager at Dynamis Advisors. He also worked as a senior explorati on and production coverage research analyst at Stifel, Nicolaus & Company. Clark began his career in Apache's treasury department in the early 1990s.
Creating sufficient and sustained fracture conductivity contributes directly to the success of acid-fracturing treatments. The permeability and mineralogy distributions of formation rocks play significant roles in creating nonuniformly etched surfaces that can withstand high closure stress. Previous studies showed that, depending on the properties of formation rock and acidizing conditions (acid selection, formation temperature, injection rate, and contact time), a wide range of etching patterns (roughness, uniform, channeling) could be created that can dictate the resultant fracture conductivity. Insoluble minerals and their distribution can completely change the outcomes of acid-fracturing treatments. However, most experimental studies use homogeneous rock samples such as Indiana limestone that do not represent the highly heterogeneous features of carbonate rocks. This work studies the effect of heterogeneity and, more importantly, the distribution of insoluble rock on acid-fracture conductivity.
In this research, we conducted acid-fracturing experiments using both homogeneous Indiana limestone samples and heterogeneous carbonate rock samples. The Indiana limestone tests served as a baseline. The highly heterogeneous carbonate rock samples contain several types of insoluble minerals such as quartz and various types of clays along sealed natural fractures. These minerals are distributed in the form of streaks correlated against the flow direction, or as smaller nodules. After acidizing the rock samples, these minerals acted as pillars that significantly reduced conductivity-decline rate at high closure stresses. Both X-ray diffraction (XRD) and X-ray fluorescence (XRF) tests were performed to pinpoint the type and location of different minerals on the fracture surfaces. A surface profilometer was also used to correlate conductivity as a function of mineralogy distribution by comparing the surface scans from after the acidizing test to the scans after the conductivity test. Theoretical models considering geostatistical correlation parameters were used to match and understand the experimental results.
Results of our study showed that insoluble minerals with higher-strength mechanical properties were not crushed at high-closure stress, resulting in a less-steep conductivity decline with an increasing closure stress. If the acid etching creates enough conductivity, the rock sample can sustain a higher closure stress with a much lower decline rate compared with Indiana limestone samples. Fracture surfaces with insoluble mineral streaks correlated against the flow direction offer the benefit of being able to maintain conductivity at high closure stress, but not necessarily high initial conductivity. Using a fracture-conductivity model with correlation length, we matched the fracture-conductivity behavior for the heterogeneous samples. Fracture surfaces with mineral streaks correlated with the flow direction could increase acid-fracturing conductivity significantly as compared to the case when the streak is correlated against the flow direction.
The results of the study show that fracture conductivity can be optimized by taking advantage of the distribution of insoluble minerals along the fracture surface and demonstrate important considerations to make the acid-fracturing treatment successful.
Because of the higher cost of scale management for subsea (SS) operations compared with platform or onshore fields, and because of the more limited opportunities for interventions, it is becoming increasingly important to obtain and use real production data from wells rather than estimated zone flow contribution from simple permeability (k) and height (h) models for scale-squeeze-treatment design.
In this paper I discuss how scale-squeeze treatments were designed (coreflood evaluation of inhibitor retention/release) and deployed for three SS heterogeneous production wells. A permeability model and a layer-height model were initially developed for each well using detailed geological log data, estimated water/oil-production rates, and the predicted water-ingress location within the wells. Two wells were each treated three times using bullhead scale-squeeze treatments, with effective scale control being reported over the designed lifetime. A production log was acquired before the fourth squeeze campaign of these two wells. This information was incorporated into the squeeze simulation to allow review of the ongoing third squeeze and enhance design accuracy for the upcoming fourth squeezes. A third well was treated twice before production-logging data became available, and the performance of treatments to this well is also assessed.
The production-logging-tool (PLT) data proved very important in changing the understanding of fluid placement and the water-ingress location during production, resulting in changes to the isotherm values used to achieve effective history match to the inhibitor returns (with PLT data incorporated in all three wells), and most significantly affecting the squeeze lifetimes. It was possible to significantly extend the treatment lifetime of two of the wells (cumulative produced water to minimum inhibitor concentration), while the treatment life of one well was greatly reduced because of the PLT-data-modified model predictions.
In this paper I outline the process of reservoir/near-wellbore modeling that is used for most initial squeeze-treatment service companies deployed in the North Sea. I will highlight in detail the value that PLT data can provide to improve the effectiveness of squeeze treatments in terms of understanding of fluid placement during squeeze deployment and water-ingress location within heterogenous production wells. The intention of this paper is to highlight the value that these types of data can provide to improve scale management (squeeze treatment and water shutoff) such that the value created more than offsets the cost of acquiring such information for SS production wells.
Gowida, Ahmed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Formation density plays a central role to identify the types of downhole formations. It is measured in the field using density logging tool either via logging while drilling (LWD) or more commonly by wireline logging, after the formations have been drilled, because of operational limitations during the drilling process that prevent the immediate acquisition of formation density.
The objective of this study is to develop a predictive tool for estimating the formation bulk density (RHOB) while drilling using artificial neural networks (ANN). The ANN model uses the drilling mechanical parameters as inputs and petrophysical well-log data for RHOB as outputs. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), standpipe pressure (SPP) and rotating speed (RPM), are measured in real time during drilling operation and significantly affected by the formation types. A dataset of 2,400 data points obtained from horizontal wells was used for training the ANN model. The obtained dataset has been divided into a 70:30 ratio for training and testing the model, respectively.
The results showed a high match with a correlation coefficient (R) between the predicted and the measured RHOB of 0.95 and an average absolute percentage error (AAPE) of 0.71%. These results demonstrated the ability of the developed ANN model to predict RHOB while drilling based on the drilling mechanical parameters using an accurate and low-cost tool. The black-box mode of the developed ANN model was converted into white-box mode by extracting a new ANN-based correlation to calculate RHOB directly without the need to run the ANN model. The new model can help geologists to identify the formations while drilling. Also, by tracking the RHOB trends obtained from the model it helps drilling engineers avoid many interrupting problems by detecting hazardous formations, such as overpressured zones, and identifying the well path, especially while drilling horizontal sections. In addition, the continuous profile of RHOB obtained from the developed ANN model can be used as a reference to solve the problem of missing and false logging data.
Petrophysical analysis of downhole logs requires accurate knowledge of matrix properties, commonly referred to as matrix adjustments. In organic-rich shale, the presence of abundant kerogen (solid and insoluble sedimentary organic matter) has a disproportionate impact on matrix properties because kerogen is compositionally distinct from all inorganic minerals that comprise the remainder of the solid matrix. As a consequence, matrix properties can be highly sensitive to kerogen properties. Moreover, the response of many downhole logs to kerogen is similar to their response to fluids. Relevant kerogen properties must be accurately known to separate tool responses to kerogen (in the matrix volume) and fluids (in the pore volume), to arrive at accurate volumetric interpretations. Unfortunately, relevant petrophysical properties of kerogen are poorly known in general and nearly always unknown in the formation of interest.
A robust method of “thermal maturity-adjusted log interpretation” replaces these unknown or assumed kerogen properties with a consistent set of relevant properties specifically optimized for the organic shale of interest, derived from only a single estimate of thermal maturity of the kerogen. The method is founded on the study of more than 50 kerogens spanning eight major oil- and gas-producing sedimentary basins, 300 Ma of depositional age, and thermal maturity from immature to dry gas (vitrinite reflectance, Ro, ranges from 0.5 to 4%). The determined kerogen properties include measured chemical (C, H, N, S, O) composition and skeletal (grain) density, as well as computed nuclear properties of apparent log density, hydrogen index, thermal- and epithermal-neutron porosities, macroscopic thermal-neutron capture cross section, macroscopic fast-neutron elastic scattering cross section, and photoelectric factor. For kerogens relevant to the petroleum industry (i.e., type II kerogen with thermal maturity ranging from early oil to dry gas), it is demonstrated that petrophysical properties are controlled mainly by thermal maturity, with no observable differences between sedimentary basins. As a result, universal curves are established relating kerogen properties to thermal maturity of the kerogen, and the curves apply equally well in all studied shale plays. Sensitivity calculations and field examples demonstrate the importance of using a consistent set of accurate kerogen properties in downhole log analysis. Thermal maturity-adjusted log interpretation provides a robust estimate of these properties, enabling more accurate and confident interpretation of porosity, saturation, and hydrocarbon in place in organic-rich shales.
A new acoustic tool has been developed to measure formation acoustic properties through casing. This measurement is important for oil and gas production in mature fields, and for wells that are cased without logging due to borehole stability issues. Conventional acoustic logging through casing in poorly bonded boreholes has been a difficult task due to the presence of overwhelming casing waves that mask the formation acoustic signal. To overcome this difficulty, we developed an acoustic tool using dual-source transmitters and the processing technique for the data acquired by the tool. This paper elaborates the operating principle of the new dual-source technology and demonstrates its application to casedhole acoustic logging. By using the dual-source design, the overwhelming casing waves from the poorly bonded casing are largely suppressed. On the basis of the casing-wave suppression and the condition that the formation is acoustically slower than casing, the formation acoustic-wave amplitude is significantly enhanced in the dual-source data-acquisition process. Subsequent processing of the data reliably obtains the acoustic velocity of the formation. The new tool has been tested in many cased wells with proven performance for various cement-bond conditions. The success of this technology makes casedhole acoustic logging an effective operation that can be routinely used to obtain reliable formation information through casing for slow to moderately fast formations.
Depth matching well logs acquired from multiple logging passes in a single well has been a longstanding challenge for the industry. The existing approaches employed in commercial platforms are typically based on classical cross-correlation and covariance measures of two signals, followed by manual adjustments. These solutions do not satisfy the rising demand to minimize user intervention to proceed towards automated data interpretation. We aimed at developing a robust and fully automatic algorithm and workflow for depth matching gamma-ray logs, which are commonly used as a proxy to match the depth of other well logs measured in multiple logging passes within the same well. This was realized by a supervised machine-learning approach through a fully connected neural network. The training dataset was obtained by manually labeling a limited set of field data. As it is unrealistic to expect a perfect model from the initial training with limited manually labeled data, we developed a continuously self-evolving depth-matching framework. During the use of depth-matching service, this framework allows taking the user input and feedback to further train and improve the depth-matching engines. This is facilitated by an automatic quality-control module for that we developed a dedicated metric by combining a few different algorithms. We use this metric to assess the quality of the returned results from the depth-matching engine. The users review the results and do manual adjustments if some intervals are not ideally depth matched by the engine. Those manual adjustments can be used to further improve the machine-learning model. A well-designed framework enables automatic and continuous self-evolving of the depth-matching service.
A key aspect of the developed framework is its generalization potential because it is independent of the signal type. It could be easily extended for other log types, especially when the correlation thereof is not obvious, provided that a sufficiently large volume of labeled data is available. This framework has been prototyped and tested on field data.
Borehole measurements are often subject to uncertainty resulting from the effects of mud-filtrate invasion. Accurate interpretation of these measurements relies on properly understanding and incorporating mud-filtrate invasion effects in the calculation of petrophysical properties. Although attempts to experimentally investigate mud-filtrate invasion and mudcake deposition have been numerous, the majority of published laboratory data are from experiments performed using linear rather than radial geometry, homogeneous rock properties, and water-based (WBM) rather than oil- or synthetic oil-based drilling mud (OBM or SOBM).
We introduce a new experimental method to accurately reproduce conditions in the borehole and near-wellbore region during, and shortly after the drilling process, when the majority of wellbore measurements are acquired. Rather than using a linear-flow apparatus, the experiments are performed using cylindrical rock cores with a hole drilled axially through the center. Radial mud-filtrate invasion takes place while injecting pressurized drilling mud into the hole at the center of the core while the outside of the core is maintained at a lower pressure. During the experiments, the core sample is rapidly and repeatedly scanned using high-resolution X-ray microcomputed tomography (micro-CT), allowing for visualization and quantification of the time-space distribution of mud filtrate and mudcake thickness. Because of the size of the core sample, the developed experimental method allows for accurate evaluation of the influence of various rock properties, such as the presence of spatial heterogeneity and fluid properties, including WBM versus OBM, on the processes of mud-filtrate invasion and mudcake deposition. Results indicate that our experimental procedure reliably captures the interplay between the spatial distributions of fluid properties and rock heterogeneities during the process of mud-filtrate invasion.
The solid skeleton of the mudcake consists of fine-grain particles; therefore, a mudcake plug is expected to have a very low permeability and a very good ability to isolate the fracture from wellbore pressure. This requires a relatively permeable formation for two reasons: Mudcake buildup requires fluid loss into the formation, and fracture pressure needs to dissipate after being isolated from the wellbore (Kumar et al. 2010).
Assaad, Wissam (Shell Global Solutions International B.V.) | Di Crescenzo, Daniele (Shell Exploration & Production Company) | Murphy, Darren (Shell Exploration & Production Company) | Boyd, John (Shell Exploration & Production Company)
In this paper, we present a method of modeling surge pressures and wave propagation that can occur during well execution. The surge pressures have an effect on formations [i.e., formation fracture resulting in mud losses and nonproductive time (NPT)]. Knowing the amplitude of surge pressure in advance can lead to operation redesign to avoid losses. Swab- and surge-pressure waves can occur at numerous events during well execution. For example, during liner operations, pressure waves can occur at dart landing or plug shearing, liner-hanger setting, or clearing a plugged shoe-track component. It is possible for surge-pressure waves to create fractures in shale and sand layers (i.e., when surge-pressure-wave amplitude exceeds formation fracturing resistance).
A transient-state physical model is built to compute pressure-wave propagation through drillstring, casing, and open hole to predict the amplitude of a surge-pressure wave and to warn when a fracture might occur in the formation, to avoid mud losses and NPT.
In the model, continuity and energy partial-differential equations (PDEs) are built for a cylindrical fluid element contained in an elastic hollow cylinder. The method of characteristics is applied to convert the PDEs to ordinary-differential equations (ODEs). The ODEs are solved numerically to compute pressure distribution along well depth and in time. The model is implemented as a graphical-user-interface (GUI) tool to be used by drilling engineers at the design phase of a well to avoid losses. The GUI tool is targeted to address different scenarios that take place during the cementation process. To date, the transient-state physical model has been applied successfully in various applications, such as monodiameter technology, running casing, and perforating operations. Two cases are studied, one for a well in the Gulf of Mexico (GOM) where mud losses have been reported, and the other for a well in Malaysia where no mud losses have occurred. Pressure-wave computations are performed with the GUI tool for the two cases. The results of both cases are presented in this paper and show that formation fracture can be predicted by the GUI tool and subsequent losses can be avoided.